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Ending the guesswork in cancer care: A CEO’s vision for predictive precision oncology
By Wolfgang Hackl M.D., Founder & CEO, OncoGenomX Inc., Switzerland
During my career as an oncologist and cancer drug developer, one question haunted me more than any other: Why do some patients respond to therapy — while others, with the same diagnosis, do not?
We have innovative treatments, biomarkers, advanced lab tests, and guidelines. But far too often, despite all that science, our treatment decisions come down to educated guesswork.
As medicine evolves, so do our data — but not always our ability to act on it with precision.
That enduring gap between what we know and what we can predict inspired me to found OncoGenomX, and to build PredictionStar™, a platform designed to redefine what precision oncology truly means.
From Biomarkers to Behavior: The Missing Link
Today, most molecular cancer tests focus on eligibility: they tell us whether a tumour expresses a particular target or carries a known mutation. This is useful, but it’s only half the story.
Eligibility does not equal efficacy.
Knowing that a patient’s tumour expresses the estrogen receptor (ER), or harbours a PIK3CA mutation, doesn’t mean it will respond to hormone therapy or PI3K inhibition. It simply means those drugs might work. And in oncology, “might” is not enough.
PredictionStar™ was built to close this precision gap — by answering not just which drugs can be used, but which will actually work.
We call this Precision Drug–Tumor Matching: the ability to segregate effective from ineffective treatments by connecting genomic and phenotypic insights into a coherent tumour profile predictive of therapeutic response.
Introducing PredictionStar™: Coherent Biomarker Intelligence
PredictionStar™ is a multidimensional tumour profiling and decision-support system powered by what we term Generative Clinical Intelligence™ — the synthesis of high-quality sequencing data and AI-driven interpretation into clear, actionable clinical guidance.
Traditional assays analyse biomarkers in isolation, treating each gene mutation or expression pattern as a separate clue.
PredictionStar™ instead identifies logically connected biomarker constellations — genomic enablers that reveal which response mechanisms are active, and phenotypic differentiators how likely the tumour will respond.
This networked approach replaces fragmented snapshots with an integrated, functional map of tumour behaviour.
It provides oncologists with something they rarely get from today’s tests: confidence. In clinical modelling, PredictionStar™ has the potential to reduce overtreatment fivefold and lower the cost of achieving one year of tumour growth control by 35 per cent.
But the numbers tell only part of the story. Behind them are patients spared from unnecessary toxicity — and doctors empowered to treat with precision instead of probability.
Built on the Technology of Giants
PredictionStar™ was designed for seamless integration into modern real-world workflows, harmonized and cross-validated to ensure reliability, and reproducibility.
The platform’s pre-sequencing tumor workup is fully standardised, minimizing inter-laboratory variability that can otherwise reach 70 per cent.
From tumour processing to data interpretation, PredictionStar™ enforces the same rigorous quality in every step, producing consistent and concordant results across labs.
As far as cloud architecture optimized for medical data privacy and global scalability we are privileged to work with world class-players of the health IOT industry
(F. Gaede, Oct 2025, Nordcloud).
A Femtech Focus: Personalising Breast Cancer Therapy
While PredictionStar™ has broad oncology applications, our first focus is hormone receptor-positive breast cancer, the most prevalent form among women.
It is here that the limits of current diagnostics are most evident — and the need for predictive and prescriptive clarity is greatest.
Even within hormone-dependent breast cancer, the most favorable form of the disease, patient outcomes vary widely. Some women respond beautifully to endocrine therapy for years, while others progress rapidly.
What makes the difference? The answers are buried in the tumour’s individual response profiles — but until now, we lacked the tools to decode them. PredictionStar™ offers that decoding ability.
Our non-interventional validation study, conducted in collaboration with clinical researchers from the Veterans Affairs Medical Centers in Cincinnati, Los Angeles, and Miami, involves data from over 4,300 patients with hormone receptor-positive disease.
By correlating predicted responses with actual treatment outcomes, we aim to establish a new clinical standard for predictive accuracy.
Our roadmap includes RUO and LDT certification in 2026, FDA-IDE clearance in Q2 2027, first RUO test sales as early as Q1 2027, and clinical study use from Q3 2027 onwards.
Redefining Precision Oncology
To understand why this matters, we need to reframe what “precision” means.
Most tests today are prognostic or eligibility-based. They classify risk or confirm target presence. PredictionStar™ adds a third, transformative dimension: functional prediction. It asks, “Which therapies will this specific tumour respond to — and how strongly?”
This evolution turns diagnostics into a true decision-support tool, enabling oncologists to design treatment compositions optimized for efficacy, rather than constrained by averages.
The distinction may seem subtle, but its impact for individuals living with breast cancer is enormous: Prognostic and eligibility tests describe. PredictionStar™ guides.
Innovation Through Unity
Our strength lies in collaboration.
I’ve often said that OncoGenomX stands “on the technology of giants, powered by the ambition to transform.” That is more than a slogan — it’s our reality. We built PredictionStar™ not as an isolated product, but as a platform for partnership.
Its architecture invites integration — with hospital systems, sequencing providers, AI developers, and pharmaceutical R&D pipelines.
In the coming years, we envision PredictionStar™ evolving into a broader family of tools: PredictionStar DX™ for predictive diagnostics, PredictionStar GCI™ for data integration and generation of actionable clinical intelligence, and PredictionStar IOT™ for real-time connectivity. Each module serves the same purpose: to transform complexity into clarity.
From Data to Decisions: A Personal Reflection
At its heart, PredictionStar™ was born from empathy.
As a clinician, I saw too many patients fall through the cracks — not because we lacked treatments, but because we lacked foresight.
Data without interpretation is noise. Our mission is to turn that noise into understanding.
When I speak with oncologists today, I sense both excitement and relief: “We will no longer be limited to maybes.” “We can begin to quantify response likelihood, combine therapies more rationally, and give patients something we cannot give today: certainty”.
Technology can be transformative, but only when anchored in purpose. For OncoGenomX, that purpose is simple — to give every patient the best possible chance at lasting response.
The Road Ahead
Our journey is just beginning. We are validating, scaling, and expanding across cancer types — from breast to prostate, lung, and beyond.
But our guiding principle remains unchanged: wherever there is cancer, there is a need for precision drug–tumour matching.
The convergence of genomics, phenomics, AI, and clinical data is redefining healthcare.
PredictionStar™ is part of that transformation — proving that predictive precision is not a futuristic concept, but an attainable standard.
We owe it to patients, to clinicians, and to science itself to make that standard universal.
In Closing
When I founded OncoGenomX, I imagined a world where no cancer patient has to live with uncertainty — where treatment is guided by prediction, not probability.
Today, that world feels within reach. PredictionStar™ is more than technology. It’s a promise:
That every patient deserves clarity. That every tumour can be understood.
And that, together, we can end the guesswork in cancer care.
Contact: Dr. Wolfgang Hackl | Founder & CEO, OncoGenomX | E-Mail | LinkedIn WH | Company Webpage | LinkedIn OGX
A new ovarian cancer drug has been approved for NHS use in England, offering hundreds of women with hard-to-treat disease a life-prolonging treatment.
Elahere is the first new drug for chemotherapy-resistant ovarian cancer to be approved by the NHS for more than 20 years.
Ovarian cancer is the 18th most common type of cancer globally, affecting more than 300,000 women a year.
More than three-quarters of patients are diagnosed at an advanced stage, making the disease harder to treat.
Prof Ruth Plummer, national clinical lead for cancer drugs at NHS England, said: “This represents the most significant breakthrough in NHS treatment for these hard-to-treat ovarian cancers in over two decades – and we’re delighted it will now offer hundreds of women much-needed hope of precious extra time with their loved ones.”
Standard treatment for ovarian cancer usually involves surgery and chemotherapy, but about 80 per cent of patients with advanced disease relapse and most eventually develop resistance to chemotherapy.
According to the National Institute for Health and Care Excellence, patients with folate receptor-alpha-positive platinum-resistant epithelial cancers have until now had limited options when their tumours stop responding to standard chemotherapy.
Now NICE has approved mirvetuximab soravtansine, also known as Elahere, for patients with epithelial ovarian, peritoneal or fallopian tube cancer that has become resistant to platinum-based chemotherapy and whose tumours contain the FRα protein that the drug targets.
FRα is a protein found on the surface of some cancer cells.
NHS England said up to 400 women a year in England could benefit, in what it described as a major milestone for treatment.
Mirvetuximab soravtansine is given through a drip once every three weeks.
A global clinical trial involving eight NHS hospitals found that the treatment delayed cancer progression and prolonged survival by an average of four months, compared with chemotherapy alone, with more manageable side-effects.
Cancer progression means the disease is growing, spreading or worsening.
In 37 per cent of patients, tumours shrank by at least 30 per cent, compared with 16 per cent of those given chemotherapy.
The drug, made by AbbVie, combines a “homing” antibody, which seeks out the FRα protein on the surface of cancer cells, with a cancer-killing molecule that destroys the cell from within.
Experts said the decision was a seminal moment and could significantly improve the quality of life of affected patients.
Rachel Downing, head of policy and external affairs at Target Ovarian Cancer, said: “This is a hugely important moment for women with platinum-resistant ovarian cancer and their families, who have faced limited effective treatment options for far too long. Today’s announcement offers real hope of improved quality of life.”
Victoria Clare, chief executive of the charity Ovacome, said: “Today marks a landmark moment. Being told that platinum-based chemotherapy is no longer working can bring anxiety and uncertainty, particularly when the disease is at an advanced stage, where time and options are limited.
“This recommendation is the first in over 20 years to offer the ovarian cancer community an additional choice at a critical stage, with the potential to make a real difference to patients and their families.”
Helen Knight, director of medicines evaluation at NICE, said: “We heard clearly from patients and clinicians about the very limited options available at this stage of the disease and the substantial burden that chemotherapy places on women’s lives.
“We are pleased that, following a robust process and a new commercial arrangement with AbbVie, we are now able to recommend this treatment for NHS use.”
Female sex may not raise stroke risk across all atrial fibrillation (AF) patients, with higher risk mainly seen in women aged 75 and older, a study suggests.
Researchers said stroke prevention for women with the condition should be more personalised, especially for patients under 75.
Dr Amitabh C Pandey, director of cardiovascular translational research at Tulane University School of Medicine, said: “For years, female sex has been included as a risk factor along with other factors such as high blood pressure and diabetes, meaning women were more likely to be prescribed anticoagulants.
“Our study shows younger women may not have as much added stroke risk as previously thought, while older women, particularly those over 75, appear to have a higher risk that deserves close attention.”
The new Tulane University study challenges a long-standing assumption in heart care that being female automatically increases stroke risk for patients with atrial fibrillation.
Atrial fibrillation, often called AF, is a common heart rhythm disorder that causes the heart to beat irregularly.
It is associated with a higher risk of stroke and is often treated with anticoagulants, also known as blood thinners.
The study found that stroke risk did not increase equally across all female patients with AF.
Instead, researchers said being female may act more as a risk modifier, with increased stroke risk seen primarily among women aged 75 and older or those with a greater burden of other health conditions.
Clinicians often use a scoring system to decide whether people with AF should be prescribed blood thinners.
The system gives points for factors including age, heart failure, diabetes, previous stroke, vascular disease and high blood pressure.
Women also receive one point for sex alone.
Researchers said this can mean women with AF become eligible for blood thinners earlier or more often than men with otherwise similar risk profiles.
While blood thinners can help prevent clot-related strokes, they can also increase the risk of bruising, prolonged bleeding, gastrointestinal bleeding and other serious complications.
The researchers analysed approximately 950,000 patients with AF using TriNetX, a large anonymised electronic health record database.
They compared stroke outcomes between male and female patients across three age groups: younger than 65, 65 to 74, and 75 and older.
Male and female patients were matched based on age, other health problems and whether they had been prescribed anticoagulation medicine.
Among patients younger than 75, the study found no significant difference in one-year stroke risk between men and women.
However, among patients aged 75 and older, women had a modest but statistically significant increase in stroke risk compared with men.
In patients aged 75 and older with no additional risk factors beyond age, women had about one additional stroke per 629 patients compared with their male counterparts.
The findings support growing interest in a newer AF risk score, known as CHA2DS2-VA, which removes sex as a standalone risk factor.
However, researchers said more studies are needed and medical guidance remains inconsistent.
Han Feng, assistant professor at Tulane University School of Medicine, said: “This general approach came from women being underrepresented in AFib trials and studies comprising only about one-third of study populations.
“Our study shows not all women with AFib have the same risk profile, and these decisions should be individualised.
Pandey said: “These findings highlight the need for modern tools and approaches that can personalise risk profiles to individuals.
“The goal is not to undertreat patients who need stroke prevention, but to better identify who is most likely to benefit from anticoagulation and who may be exposed to unnecessary risk.”
AI may help speed breast cancer diagnosis for high-risk women after abnormal mammograms, a study suggests.
Women with abnormal mammograms often wait weeks to learn whether they have breast cancer.
Researchers at UC San Francisco and UC Berkeley said an AI-guided workflow could help reduce that wait by quickly identifying those most likely to have the disease. Some women could move from imaging to evaluation, and sometimes biopsy, in a single day.
Dr Maggie Chung, first author of the study, said: “This is a really an exciting time.
“This moves us closer to personalised care, where we can tailor a plan so that each patient gets the right intervention at the right time.”
The study used an open-source AI model called Mirai.
The model was trained on hundreds of thousands of mammograms linked to patients’ cancer outcomes.
A mammogram is an X-ray scan of the breast used to look for signs of cancer. A biopsy involves taking a small tissue sample to test for disease.
The AI tool is designed to detect subtle patterns in screening mammograms and predict a woman’s cancer risk.
Researchers at UC San Francisco and UC Berkeley applied the model to more than 4,100 screening mammograms at Zuckerberg San Francisco General Hospital and Trauma Center.
Mirai identified 525 women, about 12.7 per cent of screened patients, as high risk.
Those patients could receive an interpretation of their mammograms immediately after the scan and have additional diagnostic imaging for suspicious areas on the same day.
Some women who needed biopsies were also able to have them on the same day.
The researchers said Mirai reduced the wait time for diagnostic evaluation from several weeks to about an hour.
For women who were ultimately diagnosed with breast cancer, it reduced the average wait for biopsy from more than two months to fewer than 10 days.
The researchers stressed that Mirai does not replace radiologists or make diagnoses on its own.
Instead, it acts as a triage tool to help physicians identify the patients who can benefit most from accelerated care.
The team analysed more than 114,000 archival mammograms before launching the programme, to ensure the model would capture enough high-risk patients without overloading the clinic with too many expedited evaluations.
The researchers said they hope AI will support a more personalised approach to breast cancer screening tailored to each patient’s breast cancer risk.
Chung said: “Right now, many women follow the same screening schedule but their individual risk can be very different.
“AI risk assessment gives us the chance to identify the women most likely to benefit from expedited care and get them what they need.”
Adam Yala, senior author of the study and a data scientist at UC Berkeley, said: “This is a powerful example of how AI can be a collaborative partner for physicians.
“It shows how we can improve care when we bring clinicians and data scientists together to design these systems.”
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