As precision medicine such as targeted therapy and immunotherapy often have limited accessibility, low response rate, and evolved resistance, it is urgent to develop simple, low-cost, and quick-turnaround personalized diagnostic technologies for drug response prediction with high sensitivity, speed, and accuracy. The major challenges of drug response prediction strategies employing digital database modeling are the scarcity of labeled clinical data, applicability only to a few classes of drugs, and losing the resolution at the individual patient level. Although these challenges have been partially addressed by large-scale cancer cell line datasets and more patient-relevant cell-based systems, the integration of different data types and data translation from pre-clinical to clinical utilities are still far-fetched. To overcome the current limitations of precision medicine with a clinically proven drug response prediction assay, we have developed an innovative and proprietary technology based on in vitro patient testing and in silico data analytics. First, a patient-derived gene expression signature was established via the transcriptomic profiling of cell-free mRNA (cfmRNA) from the patient’s blood. Second, a gene-to-drug data fusion and overlaying mechanism to transfer data were performed. Finally, a semi-supervised method was used for the database searching, matching, annotation, and ranking of drug efficacies from a pool of ~700 approved, investigational, or clinical trial drug candidates. A personalized drug response report can be delivered to inform clinical decisions within a week. The PGA (patient-derived gene expression-informed anticancer drug efficacy) test has significantly improved patient outcomes when compared to the treatment plans without PGA support. The implementation of PGA, which combines patient-unique cfmRNA fingerprints with drug mapping power, has the potential to identify treatment options when patients are no longer responding to therapy and when standard-of-care is exhausted.

Background: Many patients harbor minimal residual disease (MRD)—small clusters of residual tumor cells that survive therapy and evade conventional detection but drive recurrence. Although advances in molecular and computational methods have improved circulating tumor DNA (ctDNA)-based MRD detection, these approaches face challenges: ctDNA shedding fluctuates widely across tumor types, disease stages, and histological features. Additionally, low levels of driver mutations originating from healthy tissues can create background noise, complicating the accurate identification of bona fide tumor-specific signals. These limitations underscore the need for refined technologies to further enhance MRD detection beyond DNA sequences in solid malignancies. Methods: Profiling circulating cell-free mRNA (cfmRNA), which is hyperactive in tumor and non-tumor microenvironments, could address these limitations to inform postoperative surveillance and treatment strategies. This study reported the development of OncoMRD BREAST, a customized, gene signature-informed cfmRNA assay for residual disease monitoring in breast cancer. OncoMRD BREAST introduces several advanced technologies that distinguish it from the existing ctDNA-MRD tests. It builds on the patient-derived gene signature for capturing tumor activities while introducing significant upgrades to its liquid biopsy transcriptomic profiling, digital scoring systems, and tracking capabilities. Results: The OncoMRD BREAST test processes inputs from multiple cutting-edge biomarkers—tumor and non-tumor microenvironment—to provide enhanced awareness of tumor activities in real time. By fusing data from these diverse intra- and inter-cellular networks, OncoMRD BREAST significantly improves the sensitivity and reliability of MRD detection and prognosis analysis, even under challenging and complex conditions. In a proof-of-concept real-world pilot trial, OncoMRD BREAST’s rapid quantification of potential tumor activity helped reduce the risk of incorrect treatment strategies, while advanced predictive analytics contributed to the overall benefits and improved outcomes of patients. Conclusions: By tailoring the assay to individual tumor profiles, we aimed to enhance early identification of residual disease and optimize therapeutic decision-making. OncoMRD BREAST is the world’s first and only gene signature-powered test for monitoring residual disease in solid tumors.

The emergence of noninvasive liquid biopsy procedures as an alternative to surgical biopsies has fueled an intensive research effort and investment into the detection of cell‐free disease biomarkers. By pairing next‐generation sequencing (NGS) and RT‐qPCR technologies on cell‐free DNA (cfDNA) and RNA (cfRNA), respectively, we have established and validated a high‐throughput and quick‐turnaround workflow for highly sensitive detection of genomic alterations using a single tube of blood. This emphasizes the importance of our cfDNA/cfRNA recovery and enrichment knowhow to maximize the chance to capture relevant rare biomarkers. On the cfDNA‐NGS axis, sensitivity and specificity of 95% and 98% were recorded for somatic variants of ≥1% VAF, with high concordance observed between the in‐house and orthogonal assays. Overall accuracy for the cfRNA and RT‐qPCR portion of the workflow was >95%. Real‐world data of patients underwent the two‐pronged testing scheme revealed dramatic beneficial outcomes guided by genomic‐matched treatments. When implemented at the time of biopsy to expedite treatment decision‐making in the earliest possible way, the single‐tube two‐pronged approach can effectively reduce time to treatment initiation by about 70%. The cfDNA–cfRNA paired test is suitable for implementation in routine clinical decision‐making to maximize the benefit without delay.

The molecular diagnostics industry has historically relied on sanitized clinical trials and commoditized data sources to inform its biomarker discovery and validation process—an under-substantiated approach that was ultra-expensive, resource-consuming and did not reflect how representative a new biomarker would be in broader patient populations. In an effort to gain more accurate insight into the patient experience and bring innovative biomarkers to market more efficiently and accurately, the industry is now expanding into extended real-world data. To access the needed breadth and depth of patient-centric data, diagnostic companies must collaborate with a healthcare data analytics partner that has three key assets: (i) a broad and deep megadata with metadata, (ii) a data-rich provider network, and (iii) an outcomes-improvement engine to support the next generation of molecular diagnostics (Dx) and therapeutics (Rx) development.

Predicting the response of tumor cells in patients to anticancer drugs is a key issue in pharmacogenomics Recent efforts in digital computation of large scale datasets of profiling gene expression and drug sensitivity have provided a unique opportunity to address this topic However, major breakthrough to bridge gene to drug is still lacking We have developed and validated a proprietary and transformative technology, patient derived gene expression informed anticancer drug efficacy ( to establish gene drug relationship to identify patients most likely to benefit from a treatment This technology aims to integrate the new tool into the existing precision oncology testing workup The revolution from personalized gene expression to drug efficacy isarticulated around three functions blood draw, patient specific gene expression signature and customized report of effective drug(s) Preliminary trial indicated that PGA guided therapy strategy performed better than the standard of care treatment plan.

Precision cancer therapies frequently fail due to tumors’ evolving clonal diversity rather than drug efficacy. Even when initial treatment succeeds, resistance often emerges, leading to relapse. Clinicians then find themselves in the same cycle of repeating the process of testing a new drug until therapeutic exhaustion. The cycle escalates with each new treatment until no further options are available. The real-life experience of precision therapy will undeniably lead to an upgrade – from biomarker testing to drug response prediction – accordingly to favor more effective treatment options, more clinical benefit, and more patient coverage to include non-responders. While biomarker tests (or companion diagnostics) advance precision medicine by identifying only a fraction of patients as responders, drug response prediction aims to expand treatment options – particularly for non-responders – by tailoring personalized therapies to optimize outcomes while minimizing side effects. Artificial intelligence-driven approaches (e.g., deep learning and predictive modeling) leverage large datasets to generate these predictions. However, such systems remain experimental, not yet ready for clinical use. Patient-derived gene expression-informed anticancer drug efficacy (PGA) is the ultimate answer to the unmet clinical need for a quick turnaround and cost-efficient drug response prediction technology. With PGA, therapeutic non-responders now are able to benefit from more drug options than ever before. Since the technology is fitted with patient testing, gene activity detection, data mapping, drug matching, and efficacy ranking capabilities, clinicians can be quickly notified of potentially effective drugs, winning the decisive time for decision-making.