Predicting survival of pancreatic cancer patients treated with gemcitabine using longitudinal tumour size data

Prognostic models to predict survival in patients with pancreatic cancer: a systematic review

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) has poor survival. Current treatments offer little likelihood of cure or long-term survival. This systematic review evaluates prognostic models predicting overall survival in patients diagnosed with PDAC.

METHODS

We conducted a comprehensive search of eight electronic databases from their date of inception through to December 2019. Studies that published models predicting survival in patients with PDAC were identified.

RESULTS

3297 studies were identified; 187 full-text articles were retrieved and 54 studies of 49 unique prognostic models were included. Of these, 28 (57.1%) were conducted in patients with advanced disease, 17 (34.7%) with resectable disease, and four (8.2%) in all patients. 34 (69.4%) models were validated, and 35 (71.4%) reported model discrimination, with only five models reporting values >0.70 in both derivation and validation cohorts. Many (n = 27) had a moderate to high risk of bias and most (n = 33) were developed using retrospective data. No variables were unanimously found to be predictive of survival when included in more than one study.

CONCLUSION

Most prognostic models were developed using retrospective data and performed poorly. Future research should validate instruments performing well locally in international cohorts and investigate other potential predictors of survival.

Predicting tumour growth and its impact on survival in gemcitabine-treated patients with advanced pancreatic cancer

The aim of this evaluation was to characterize the impact of the tumour size (TS) effects driven by the anticancer drug gemcitabine on overall survival (OS) in patients with advanced pancreatic cancer by building and validating a predictive semi-mechanistic joint TS-OS model. TS and OS data were obtained from one phase II and one phase III study where gemcitabine was administered (1000-1250 mg/kg over 30-60 min i.v infusion) as single agent to patients (n = 285) with advanced pancreatic cancer. Drug exposure, TS and OS were linked using the population approach with NONMEM 7.3. Pancreatic tumour progression was characterized by exponential growth (doubling time = 67 weeks), and tumour response to treatment was described as a function of the weekly area under the gemcitabine triphosphate concentration vs time curve (AUC), including treatment-related resistance development. The typical predicted percentage of tumour growth inhibition with respect to no treatment was 22.3% at the end of 6 chemotherapy cycles. Emerging resistance elicited a 57% decrease in drug effects during the 6th chemotherapy cycle. Predicted TS profile was identified as main prognostic factor of OS, with tumours responders' profiles improving median OS by 30 weeks compared to stable-disease TS profiles. Results of NCT00574275 trial were predicted using this modelling framework, thereby validating the approach as a prediction tool in clinical development. Our analyses show that despite the advanced stage of the disease in this patient population, the modelling framework herein can be used to predict the likelihood of treatment success using early clinical data.

On the relationship between tumour growth rate and survival in non-small cell lung cancer

A recurrent question within oncology drug development is predicting phase III outcome for a new treatment using early clinical data. One approach to tackle this problem has been to derive metrics from mathematical models that describe tumour size dynamics termed re-growth rate and time to tumour re-growth. They have shown to be strong predictors of overall survival in numerous studies but there is debate about how these metrics are derived and if they are more predictive than empirical end-points. This work explores the issues raised in using model-derived metric as predictors for survival analyses. Re-growth rate and time to tumour re-growth were calculated for three large clinical studies by forward and reverse alignment. The latter involves re-aligning patients to their time of progression. Hence, it accounts for the time taken to estimate re-growth rate and time to tumour re-growth but also assesses if these predictors correlate to survival from the time of progression. I found that neither re-growth rate nor time to tumour re-growth correlated to survival using reverse alignment. This suggests that the dynamics of tumours up until disease progression has no relationship to survival post progression. For prediction of a phase III trial I found the metrics performed no better than empirical end-points. These results highlight that care must be taken when relating dynamics of tumour imaging to survival and that bench-marking new approaches to existing ones is essential.

Dose Rationalization of Pembrolizumab and Nivolumab Using Pharmacokinetic Modeling and Simulation and Cost Analysis

Pembrolizumab and nivolumab are highly selective anti-programmed cell death 1 (PD-1) antibodies approved for the treatment of advanced malignancies. Variable exposure and significant wastage have been associated with body size dosing of monoclonal antibodies (mAbs). The following dosing strategies were evaluated using simulations: body weight, dose banding, fixed dose, and pharmacokinetic (PK)-based methods. The relative cost to body weight dosing for band, fixed 150 mg and 200 mg, and PK-derived strategies were -15%, -25%, + 7%, and -16% for pembrolizumab and -8%, -6%, and -10% for band, fixed, and PK-derived strategies for nivolumab, respectively. Relative to mg/kg doses, the median exposures were -1.0%, -4.6%, + 27.1%, and +3.0% for band, fixed 150 mg, fixed 200 mg, and PK-derived strategies, respectively, for pembrolizumab and -3.1%, + 1.9%, and +1.4% for band, fixed 240 mg, and PK-derived strategies, respectively, for nivolumab. Significant wastage can be reduced by alternative dosing strategies without compromising exposure and efficacy.

Factors influencing survival of patients with pancreatic adenocarcinoma and synchronous liver metastases receiving palliative care

BACKGROUND AND AIMS

Patients with pancreatic ductal adenocarcinoma and synchronous liver metastases (PACLM) have an extremely limited life expectancy. We performed a single-center analysis to explore the clinical results and prognostic factors of patients with PACLM receiving palliative care.

METHODS

We retrospectively reviewed 189 patients undergoing palliative care at Tianjin Medical University Cancer Hospital over a 15-year period. Clinical characteristics, survival condition, and factors associated with survival were analyzed. Treatment methods included palliative bypass surgery, percutaneous transhepatic cholangiodrainage, drug analgesia, symptomatic treatment, and other nutritional or supportive measures.

RESULTS

The overall survival (OS) was 3.6 months for all patients. Multivariate analysis for clinical features showed that Karnofsky performance score (KPS), ascites, cigarette smoking, primary tumor size, and lactate dehydrogenase (LDH) were prognostic variables with statistical significance (P < 0.05). The patients were classified into three groups of patients according to how many of these 5 risk factors were present: 0-1, 2, or 3-5 risk factors. The median OS of the 3 groups of patients were 5.0, 3.3, and 2.5 months, respectively, with a notable statistical significance (P < 0.0001).

CONCLUSIONS

KPS<80, ascites, cigarette smoking, primary tumor size≥5 cm, and LDH≥250U/L are effective predictive factors of poor prognosis for patients with PACLM. The stratification of treatment outcome groups based on these factors facilitates evaluation of individual prognosis and can guide clinical decisions.