Dharminder Singh Chahal, Chief Executive Officer at SkylineDx, discusses the real-world applications of how SkylineDx bridges biomarker insights to therapeutic development.

Real-world applications  

As the healthcare landscape continues to evolve toward personalised, data-driven care, clinically validated diagnostic tests with real-world data integration are redefining how diseases are diagnosed, stratified, and treated. Recent advances in genomics have significantly reshaped the landscape of drug discovery and development. High-throughput sequencing technologies (1), including whole-genome and transcriptome profiling, have enabled researchers to uncover novel biomarkers, explain disease mechanisms, and identify therapeutic targets with unprecedented precision. Transcriptomics has emerged as a powerful tool in therapeutic development, allowing for dynamic monitoring of gene expression changes in response to treatment and disease progression. SkylineDx’s gene signature-based diagnostics demonstrate how gene expression data can be translated into clinically actionable insights, bridging the gap between molecular research to diagnostic products that provide personalised risk assessment to guide patient pathway decision-making. 

By developing and commercialising molecular assays, clinicians can make personalised treatment decisions across multiple disease states. Its gene expression profiling (GEP) predicts nodal metastasis risk in melanoma, and SKY92, a test that stratifies multiple myeloma patients by relapse risk. These tools enable more precise risk assessment and therapeutic planning, improving outcomes and reducing unnecessary interventions. 

Tools for treatment decision-making 

The Merlin Assay is a diagnostic test which combines clinical and pathological variables with gene expression profiling (CP-GEP) to guide sentinel lymph node biopsy (SLNB) decision-making in melanoma. Using the CP-GEP model, Merlin integrates gene expression data with Breslow thickness and patient age to generate a probabilistic score that classifies patients as high or low risk for nodal metastasis. The assay begins with RNA extraction from formalin-fixed paraffin-embedded primary melanoma tissue, followed by a reverse transcriptase step, and qPCR analysis targeting eight melanoma genes and metastasis-associated genes. Next, gene expression values are processed through a logistic regression algorithm alongside CP data to produce an actionable binary risk classification: low risk or high risk for nodal metastasis. The reporting turnaround time of the test is within five working days if using the diagnostic service (available in United States and Europe), alternatively the test can also be performed in an onsite in a molecular lab in approximately six hours in Europe only. 

This assay’s clinical performance is supported by robust evidence. A prospective Dutch multicentre study (2) involving 260 patients showed an overall negative predictive value (NPV) of 96.7%, with a potential SLNB reduction rate of 42.2% in patients with pT1-pT3 melanoma. The landmark MERLIN_001 prospective trial, which enrolled 1,686 patients across nine US sites, confirmed a 92.9% NPV, with 37% of patients classified as low risk in the entire population. In addition, the study showed success in risk stratifying thin melanoma patients based on their risk of nodal metastasis. An independent systematic review and meta-analysis (3) by the Melanoma Institute Australia authors concluded that the assay is an effective deselection tool for SLNB to safely identify patients with low-intermediate risk primary cutaneous melanomas who do not require SLNB, reducing both patient morbidity and costs.  

In addition to predicting nodal metastasis, prognostic capabilities have also been shown in serval publications, such as Eggermont 2020 (4), Mulder 2022 (5), Amaral 2023 (6), and Amaral 2025 (7). Studies show that low-risk patients have significantly better survival than high-risk patients, for example: Merlin risk stratification of SLNB negative patients showed a five-year relapse free survival of 93% (8) for low-risk patients compared to 70% in high-risk patients.  

Lastly, a German cohort study (9) including 930 clinical stage I/II patients, shows the assay’s ability to predict risk of relapse beyond nodal status as these patients did not undergo SLNB. Patients in the high-risk group were around 20 times more likely to experience a recurrence, compared to low risk. Additionally, Merlin High-Risk patients showed significantly increased rates of distant metastasis and melanoma-specific death. The assay is widely accessible across the US and Europe. 

The SKY92 assay brings similar precision to multiple myeloma care. This GEP test stratifies patients into high-risk and standard-risk categories at diagnosis and relapse by analysing the expression of 92 genes in purified CD138+ plasma cells. The process begins with a bone marrow aspirate, which is processed within 32 hours to isolate the relevant cells. Extracted RNA is hybridised to a GeneChip microarray or equivalent platform, and the resulting data undergoes quality control and normalisation.  

A SkylineDx-designed algorithm then calculates a continuous risk score, classifying patients above a predefined threshold as high-risk—indicating a greater likelihood of early progression and poorer overall survival. Results are delivered within five working days, enabling timely and informed treatment planning. Generally, guidelines recommend treating high-risk patients more aggressively, therefore, by revealing tumour biology that conventional cytogenetics may miss, SKY92 supports more effective frontline and maintenance therapy decisions. 

Addressing unmet needs in precision medicine 

Despite advances in targeted therapies, many patients still undergo unnecessary procedures or receive ineffective treatments due to the lack of upfront stratification tools. For example, in cutaneous melanoma, SLNB is often recommended for tumours 0.8 mm or smaller, yet the likelihood of finding positive lymph nodes is low – as 80 to 85% of the biopsies come back negative – while the procedure’s risk of complications is 17%. Additionally, we know that in multiple myeloma, 20-25% of patients do not respond adequately to standard of care treatment or relapse early. Genomics-based diagnostic tools like Merlin and SKY92 are particularly valuable because they can include or exclude specific patient groups, preventing over- or undertreatment. With tools like these, SkylineDx aims to close information gaps by offering early, accurate risk stratification, predictive biomarkers to guide therapy selection, and clinically validated assays that accelerate the translation of academic discoveries into reimbursed diagnostics. These efforts support more efficient resource allocation and better patient outcomes in value-based care models. 

Role of genomics in drug development and personalised care 

As the pharmaceutical industry increasingly embraces precision medicine, genomic diagnostic tools like Merlin and SKY92 are not only guiding clinical decisions but can also inform drug pipeline strategies. By identifying patient subgroups most (un)likely to benefit from specific interventions, these tools contribute to more efficient resource allocation and personalised treatment pathways. Merlin and SKY92 are representative of a larger paradigm shift, where genomic insights are central to the future of drug development and individualised healthcare. 

In early-stage drug discovery, gene expression insights can reveal novel therapeutic targets by explaining disease mechanisms and identifying dysregulated pathways. A Frontiers in Genetics (10) review highlights how high-throughput transcriptome profiling is used to uncover therapeutic targets and dysregulated pathways in drug discovery. This helps with the transition from biomarker discovery to drug candidate selection. During clinical development, diagnostics can support trial designs by providing real-time patient insights. SkylineDx’s research model includes partnerships with academic institutions to transition from biomarker discovery to validated diagnostics, supporting clinical trial design and regulatory approval as successfully shown with Merlin and SKY92 assays – which started off as academic papers and transformed to market products. 

By implanting diagnostics into drug development, SkylineDx and similar companies are playing a role in advancing precision therapeutics. SkylineDx’s collaborations with biopharma companies and research institutions, such as the Mayo Clinic, highlight how genomic data can bridge the gap between laboratory research and clinical application, ultimately leading to more personalised, effective, and economically sustainable solutions. 

Beyond diagnostic validation, SkylineDx works with multiple partners to integrate its diagnostics into therapeutic studies. These partnerships facilitate the co-development of companion diagnostics and support regulatory submissions by providing robust clinical evidence. A commitment to rigorous validation ensures that diagnostic tools are not only reimbursable and guideline-aligned, but also instrumental in shaping the future of personalised drug development. 

Impact through innovation 

SkylineDx’s innovation pipeline is structured around three R&D programmes: Falcon, Panthera, and Delphia – alongside the STRIX biomarker discovery platform.  

• Falcon focuses on dermato-oncology, building on the success of the Merlin Assay and expanding into new indications such as cutaneous squamous cell carcinoma. By identifying genomic markers associated with disease progression and treatment response, Falcon can support the development of targeted therapies and companion diagnostics that can guide clinical decision-making and improve patient outcomes. 

• Panthera’s concentration is on haemato-oncology with the SKY92 assay and exploring its integration into treatment for multiple myeloma and other haematologic malignancies. This programme plays a critical role in therapeutic innovation by enabling early risk detection, informing therapy choices, and supporting clinical trials with molecularly defined patient cohorts. 

• Delphia targets infectious and inflammatory diseases, with a diagnostic for Kawasaki disease among its top priorities. By working with immune-related biomarkers, Delphia will contribute to the development of immunomodulatory therapies and supports precision approaches in areas where conventional diagnostics fall short. 

• The STRIX platform supports all three programmes by systematically screening candidate biomarkers. This discovery engine accelerates the translation of molecular insights into clinically validated assays and therapeutic targets. STRIX’s integration of multi-omics data enhances the identification of druggable pathways and supports the development of novel therapeutics. 

Together, these programmes illustrate the bridge between diagnostics and therapeutics. By aligning biomarker discovery with clinical utility and drug development, companies like SkylineDx are fostering a more integrated and efficient approach to personalised medicine. SkylineDx’s pipeline not only expands the reach of precision diagnostics, but also informs pharmaceutical strategies, enabling the creation of targeted treatments that are tailored to individual patient profiles. 

Advancing precision diagnostics 

SkylineDx is actively developing next-generation diagnostic assays integrating gene expression data that may be combined with clinical and pathological variables to further personalise patient care. For instance, they are developing a diagnostic test for squamous cell carcinoma and, through a global patent license agreement with Imperial College London, diagnostic tools for Kawasaki disease. These initiatives demonstrate the company’s diversified approach to addressing patient and physician needs across oncology and paediatric inflammatory conditions.  

In the field, integration of artificial intelligence and machine learning with multi-omics data, including genomics, proteomics, and metabolomics, is accelerating the identification of druggable targets and optimising clinical trial design. A recent study found that AI-discovered drugs in Phase I clinical trials have a success rate of 80–90%, compared to just 40–65% (11) for traditionally discovered drugs. These technologies support adaptive trial frameworks, patient stratification, and predictive modeling of treatment response, thereby reducing development timelines and improving success rates. 

Recent advances in genomics are changing the drug discovery and development process and enabling a shift from population-based approaches to precision-targeted therapies. These have wide-ranging applications across research and clinical development, serving as tools in biomarker discovery, patient stratifications, and therapeutic response prediction. By integrating gene expression into early-stage research and trail design, they help identify high-value targets, refine inclusion criteria, and support more efficient decisions.  

About the author

Dharminder Singh Chahal is an experienced life sciences executive and investor with a strong track record in diagnostics, biotechnology, and healthcare investment. He is the CEO and Founder of SkylineBx DV, a Rotterdam and San Diego based commercial stage biotechnology company developing advanced molecular diagnostic solutions for oncology and infectious diseases.

References

1: Yang et al Front. Genet., 05 February 2020 Sec. Predictive Toxicology and Exposomics 

2: Stassen, Robert C. et al. European Journal of Surgical Oncology, Volume 49, Issue 12  

3: Wong et al Critical Reviews in Oncology/Hematology,Volume 214, 2025, 

4: Eggermont, Alexander M.M. et al. European Journal of Cancer, Volume 140, 11 – 18 

5: Mulder et al. Cancers (Basel). 2022 Jun 9 

6: Amaral et al. Eur J Cancer. 2023 Mar;182:155-162  

7: Teresa Amaral et al., European Journal of Cancer 

8: Amaral T et al.  Eur J Cancer. 2023 Mar;182:155-162.  

9: Amaral, Teresa et al. European Journal of Cancer, Volume 220, 115372 

10: Yang et al. Front. Genet., 05 February 2020, Sec. Predictive Toxicology and Exposomics, Volume 11 – 2020.  

11: Nicole A. Colwell, MD, Association of Cancer Care Centers BuzzBlog Dec 2024