Study of prognostic markers in advanced cancer

Abstract

BACKGROUND: Prognostication is a core skill fundamental to the clinical management of patients with advanced cancer. This skill is exercised to guide appropriate clinical decisions, plan supportive services and allocate resource utilisation.

Prognostication by clinicians is often erroneous, optimistic, informal and subjective. Clinicians base survival predictions upon clinical experience, clinical intuition and knowledge of cancer trajectories. Prognostic factors have been identified and validated in patients with cancer. These can be clinical markers or biomarkers. Clinical markers including weight loss and Performance Status (PS), and biomarkers such as C-reactive protein (CRP), lactate dehydrogenase (LDH), White cell count (WCC) and albumin, all representative of systemic inflammation, have been shown to be predictive of survival. Several prognostic factors have been combined to develop prognostic tools to improve prognostication accuracy. The aims were to examine all these prognostic markers and the tools, to clarify which prognostic markers are most predictive of survival in advanced cancer.

METHODS: To meet these aims a systematic review, an analysis of a prospectively collected biobank of patients with lung cancer and finally a large de novo multi-centre (UK) observational cohort study (Inflammatory biomarkers in Prognosis in Advanced Cancer [IPAC] study), were undertaken. The latter examined prognostic factors and was informed by the systematic review and biobank analysis. The prognostic factors evaluated throughout included demographic factors, disease characteristics, clinical factors and biomarkers. Literature appraisal and synthesis, survival analysis and logistic regression methods were employed as appropriate.

RESULTS: The systematic review concluded that numerous prognostic tools predict survival in patients with advanced cancer; however comparison was difficult due to the heterogeneity of the tools and the methods used to determine their accuracy. Some tools incorporate prognostic factors that have been independently validated to be of prognostic significance in advanced cancer whilst other tools may include some factors which are not validated. The prognostic tools demonstrating greatest accuracy in determining survival are the Palliative Performance Scale (PPS), the Palliative Prognostic Score (PaP), the Palliative Prognostic Index (PPI), and the Glasgow Prognostic Score (GPS) including the modified variant (mGPS). These tools have all been externally validated in more than 2000 patients with advanced cancer and were independently associated with survival (p<0.001).

The biobank analysis identified the markers (clinical and biomarkers) which are most predictive of survival in advanced lung cancer. The prognostic markers included in many of the prognostic tools with greatest survival prediction accuracy are PS and mGPS (p<0.001).

A prospectively acquired biobank identified the markers (clinical and biomarkers) which are most predictive of survival in advanced incurable lung cancer. The prognostic markers which are included in many of the prognostic tools with greatest survival prediction accuracy are PS and mGPS.

The prospective observational study demonstrated that CPS (Clinician Predicted Survival), mGPS, ECOG-PS (Eastern Cooperative Oncology Group - Performance Status), dyspnoea, Global Health, cognitive impairment, anorexia, weight loss, LDH, WCC and neutrophil count (NC) predicted survival at 30 days (univariate analysis). CPS, ECOG-PS, mGPS, dyspnoea, Global Health, cognitive impairment, anorexia, weight loss, LDH, WCC and NC, predicted survival at 3 months. On multivariate analysis, ECOG-PS, mGPS and neutrophil count predicted survival at 30 days while ECOG-PS, mGPS, weight loss, LDH and WCC predicted survival at 3 months.

CONCLUSION: In patients with advanced cancer, the most accurate prognostic factors include clinical markers (Performance Status, weight loss) and biomarkers of the systemic inflammatory response (CRP and albumin [combined in the mGPS], NC, WCC). The next step in this work is assessing how these can be utilised in clinical practice.