Multivariable prognostic modelling to improve prediction of colorectal cancer recurrence: the PROSPeCT trial

Abstract

Objective: Improving prognostication to direct personalised therapy remains an unmet need. This study prospectively investigated promising CT, genetic, and immunohistochemical markers to improve the prediction of colorectal cancer recurrence.

Material and methods: This multicentre trial (ISRCTN 95037515) recruited patients with primary colorectal cancer undergoing CT staging from 13 hospitals. Follow-up identified cancer recurrence and death. A baseline model for cancer recurrence at 3 years was developed from pre-specified clinicopathological variables (age, sex, tumour-node stage, tumour size, location, extramural venous invasion, and treatment). Then, CT perfusion (blood flow, blood volume, transit time and permeability), genetic (RAS, RAF, and DNA mismatch repair), and immunohistochemical markers of angiogenesis and hypoxia (CD105, vascular endothelial growth factor, glucose transporter protein, and hypoxia-inducible factor) were added to assess whether prediction improved over tumour-node staging alone as the main outcome measure.

Results: Three hundred twenty-six of 448 participants formed the final cohort (226 male; mean 66 ± 10 years. 227 (70%) had ≥ T3 stage cancers; 151 (46%) were node-positive; 81 (25%) developed subsequent recurrence. The sensitivity and specificity of staging alone for recurrence were 0.56 [95% CI: 0.44, 0.67] and 0.58 [0.51, 0.64], respectively. The baseline clinicopathologic model improved specificity (0.74 [0.68, 0.79], with equivalent sensitivity of 0.57 [0.45, 0.68] for high vs medium/low-risk participants. The addition of prespecified CT perfusion, genetic, and immunohistochemical markers did not improve prediction over and above the clinicopathologic model (sensitivity, 0.58-0.68; specificity, 0.75-0.76).

Conclusion: A multivariable clinicopathological model outperformed staging in identifying patients at high risk of recurrence. Promising CT, genetic, and immunohistochemical markers investigated did not further improve prognostication in rigorous prospective evaluation.

Clinical relevance statement: A prognostic model based on clinicopathological variables including age, sex, tumour-node stage, size, location, and extramural venous invasion better identifies colorectal cancer patients at high risk of recurrence for neoadjuvant/adjuvant therapy than stage alone.

Key points: Identification of colorectal cancer patients at high risk of recurrence is an unmet need for treatment personalisation. This model for recurrence, incorporating many patient variables, had higher specificity than staging alone. Continued optimisation of risk stratification schema will help individualise treatment plans and follow-up schedules.

Keywords: Angiogenesis; CT-perfusion; Large bowel; Prognostic model, Neoplasms/primary.

Fig. 1

Flowchart showing participant flow through the trial. n, number; ne, not evaluable. *Note: neoadjuvant therapy included chemoradiotherapy, radiotherapy alone and chemotherapy alone

Fig. 2

Forest plot of sensitivity and specificity, and 95% CI, for disease recurrence for standard AJCC tumour-node staging (rule C) compared to the baseline clinicopathological model (model A). Data are also shown for the various models incorporating CT perfusion imaging markers, or genetic/immunohistochemical markers to the baseline clinicopathological model. AJCC, American Joint Committee on Cancer; PCT, CT perfusion; IHC, immunohistochemistry

Fig. 3

Kaplan–Meier (K–M) curves for Model A (baseline clinicopathological variables), Model B (baseline + CT perfusion variables), and Model F (baseline + pathology variables). A K–M curve for standard clinicopathological variables (Model A) at three different risk groupings defined by the prediction index. The graphs shown are respectively: high vs medium vs low risk and high vs medium/low risk. The high-risk group consisted the 33% of participants with the highest prediction. B K–M curves for Model B (i.e. Model A plus CT perfusion variables assessed at local sites). The distribution of risk groupings is similar to Model A alone, indicating that model prediction is not improved significantly by the addition of CT perfusion variables derived by local site analysis. C K–M curves for Model F (i.e. Model A + all novel immunohistochemical and genetic marker variables). The distribution of risk groupings is similar to Model A alone, indicating that model prediction is not improved significantly by the addition of novel pathology variables. Note: primary outcome: data beyond the 3-year time-point is sparse and should not be over-interpreted