A set of molecular markers predicts chemosensitivity to Mitomycin-C following cytoreductive surgery and hyperthermic intraperitoneal chemotherapy for colorectal peritoneal metastasis.
Nicholas Brian ShannonJoey Wee-Shan TanHwee Leong TanWeining WangYudong ChenHui Jun LimQiu Xuan TanJosephine HendriksonWai Har NgLi Yang LooThakshayeni SkanthakumarSeettha D WasudevanOi Lian KonTony Kiat Hon LimGrace Hwei Ching TanClaramae Shulyn ChiaKhee Chee SooChin-Ann Johnny OngMelissa Ching Ching TeoPublished in: Scientific reports (2019)
Cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) is associated with significant perioperative morbidity and mortality. We aim to generate and validate a biomarker set predicting sensitivity to Mitomycin-C to refine selection of patients with colorectal peritoneal metastasis (CPM) for this treatment. A signature predicting Mitomycin-C sensitivity was generated using data from Genomics of Drug Sensitivity in Cancer and The Cancer Genome Atlas. Validation was performed on CPM patients who underwent CRS-HIPEC (n = 62) using immunohistochemistry (IHC). We determined predictive significance of our set using overall survival as a surrogate endpoint via a logistic regression model. Three potential biomarkers were identified and optimized for IHC. Patients exhibiting lower expression of PAXIP1 and SSBP2 had poorer survival than those with higher expression (p = 0.045 and 0.140, respectively). No difference was observed in patients with differing DTYMK expression (p = 0.715). Combining PAXIP1 and SSBP2 in a set, patients with two dysregulated protein markers had significantly poorer survival than one or no dysregulated marker (p = 0.016). This set independently predicted survival in a Cox regression model (HR 5.097; 95% CI 1.731-15.007; p = 0.003). We generated and validated an IHC prognostic set which could potentially identify patients who are likely to benefit from HIPEC using Mitomycin-C.
Keyphrases
- end stage renal disease
- peritoneal dialysis
- ejection fraction
- minimally invasive
- poor prognosis
- prognostic factors
- emergency department
- machine learning
- binding protein
- acute kidney injury
- radiation therapy
- small molecule
- gene expression
- deep learning
- artificial intelligence
- young adults
- rectal cancer
- electronic health record
- lymph node metastasis
- surgical site infection
- smoking cessation
- squamous cell
- childhood cancer
- adverse drug
- replacement therapy