Molecular profiling of cancer patients enables personalized combination therapy: the I-PREDICT study.
Jason K SicklickShumei KatoRyosuke OkamuraMaria SchwaederleMichael E HahnCasey B WilliamsPradip DeAmy KrieDavid E PiccioniVincent A MillerJeffrey S RossAdam BensonJennifer WebsterPhilip J StephensJiun-Kae Jack LeePaul T FantaScott M LippmanBrian Leyland-JonesRazelle KurzrockPublished in: Nature medicine (2019)
Cancer treatments have evolved from indiscriminate cytotoxic agents to selective genome- and immune-targeted drugs that have transformed the outcomes of some malignancies1. Tumor complexity and heterogeneity suggest that the 'precision medicine' paradigm of cancer therapy requires treatment to be personalized to the individual patient2-6. To date, precision oncology trials have been based on molecular matching with predetermined monotherapies7-14. Several of these trials have been hindered by very low matching rates, often in the 5-10% range15, and low response rates. Low matching rates may be due to the use of limited gene panels, restrictive molecular matching algorithms, lack of drug availability, or the deterioration and death of end-stage patients before therapy can be implemented. We hypothesized that personalized treatment with combination therapies would improve outcomes in patients with refractory malignancies. As a first test of this concept, we implemented a cross-institutional prospective study (I-PREDICT, NCT02534675 ) that used tumor DNA sequencing and timely recommendations for individualized treatment with combination therapies. We found that administration of customized multidrug regimens was feasible, with 49% of consented patients receiving personalized treatment. Targeting of a larger fraction of identified molecular alterations, yielding a higher 'matching score', was correlated with significantly improved disease control rates, as well as longer progression-free and overall survival rates, compared to targeting of fewer somatic alterations. Our findings suggest that the current clinical trial paradigm for precision oncology, which pairs one driver mutation with one drug, may be optimized by treating molecularly complex and heterogeneous cancers with combinations of customized agents.
Keyphrases
- combination therapy
- cancer therapy
- clinical trial
- machine learning
- single cell
- type diabetes
- drug delivery
- randomized controlled trial
- single molecule
- deep learning
- genome wide
- stem cells
- copy number
- squamous cell carcinoma
- mesenchymal stem cells
- weight loss
- newly diagnosed
- transcription factor
- clinical practice
- replacement therapy
- multidrug resistant
- patient reported