Validated Nomogram Predicting 6-Month Survival in Pancreatic Cancer Patients Receiving First-Line 5-Fluorouracil, Oxaliplatin, and Irinotecan

Constructing a Novel Amino Acid Metabolism Signature: A New Perspective on Pheochromocytoma Diagnosis, Immune Landscape, and Immunotherapy

Pheochromocytoma/paraganglioma (PGPG) is a rare neuroendocrine tumor. Amino acid metabolism is crucial for energy production, redox balance, and metabolic pathways in tumor cell proliferation. This study aimed to build a risk model using amino acid metabolism-related genes, enhancing PGPG diagnosis and treatment decisions. We analyzed RNA-sequencing data from the PCPG cohort in the GEO dataset as our training set and validated our findings using the TCGA dataset and an additional clinical cohort. WGCNA and LASSO were utilized to identify hub genes and develop risk prediction models. The single-sample gene set enrichment analysis, MCPCOUNTER, and ESTIMATE algorithm calculated the relationship between amino acid metabolism and immune cell infiltration in PCPG. The TIDE algorithm predicted the immunotherapy efficacy for PCPG patients. The analysis identified 292 genes with differential expression, which are involved in amino acid metabolism and immune pathways. Six genes (DDC, SYT11, GCLM, PSMB7, TYRO3, AGMAT) were identified as crucial for the risk prediction model. Patients with a high-risk profile demonstrated reduced immune infiltration but potentially higher benefits from immunotherapy. Notably, DDC and SYT11 showed strong diagnostic and prognostic potential. Validation through quantitative Real-Time Polymerase Chain Reaction and immunohistochemistry confirmed their differential expression, underscoring their significance in PCPG diagnosis and in predicting immunotherapy response. This study's integration of amino acid metabolism-related genes into a risk prediction model offers critical clinical insights for PCPG risk stratification, potential immunotherapy responses, drug development, and treatment planning, marking a significant step forward in the management of this complex condition.

Construction and validation of a nomogram for cancer specific survival of postoperative pancreatic cancer based on the SEER and China database

BACKGROUND

The recurrence rate and mortality rate among postoperative pancreatic cancer patients remain elevated. This study aims to develop and validate the cancer-specific survival period for individuals who have undergone pancreatic cancer surgery.

METHODS

We extracted eligible data from the Surveillance, Epidemiology, and End Results database and randomly divided all patients into a training cohort and an internal validation cohort. External validation was performed using a separate Chinese cohort. The nomogram was developed using significant risk factors identified through univariate and multivariate Cox proportional hazards regression. The effectiveness of the nomogram was assessed using the area under the time-dependent curve, calibration plots, and decision curve analysis. Kaplan-Meier survival curves were utilized to visualize the risk stratification of nomogram and AJCC stage.

RESULTS

Seven variables were identified through univariate and multivariate analysis to construct the nomogram. The consistency index of the nomogram for predicting overall survival was 0.683 (95% CI: 0.675-0.690), 0.689 (95% CI: 0.677-0.701), and 0.823 (95% CI: 0.786-0.860). The AUC values for the 1- and 2-year time-ROC curves were 0.751 and 0.721 for the training cohort, 0.731 and 0.7554 for the internal validation cohort, and 0.901 and 0.830 for the external validation cohorts, respectively. Calibration plots demonstrated favorable consistency between the predictions of the nomogram and actual observations. Moreover, the decision curve analysis indicated the clinical utility of the nomogram, and the risk stratification of the nomogram effectively identified high-risk patients.

CONCLUSION

The nomogram guides clinicians in assessing the survival period of postoperative pancreatic cancer patients, identifying high-risk groups, and devising tailored follow-up strategies.

A review of the utility of prognostic tools in predicting 6-month mortality in cancer patients, conducted in the context of voluntary assisted dying

BACKGROUND

Eligibility to access the Victorian voluntary assisted dying (VAD) legislation requires that people have a prognosis of 6 months or less (or 12 months or less in the setting of a neurodegenerative diagnosis). Yet prognostic determination is frequently inaccurate and prompts clinician discomfort. Based on functional capacity and clinical and biochemical markers, prognostic tools have been developed to increase the accuracy of life expectancy predictions.

AIMS

This review of prognostic tools explores their accuracy to determine 6-month mortality in adults when treated under palliative care with a primary diagnosis of cancer (the diagnosis of a large proportion of people who are requesting VAD).

METHODS

A systematic search of the literature was performed on electronic databases Medline, Embase and Cinahl.

RESULTS

Limitations of prognostication identified include the following: (i) prognostic tools still provide uncertain prognoses; (ii) prognostic tools have greater accuracy predicting shorter prognoses, such as weeks to months, rather than 6 months; and (iii) functionality was often weighted significantly when calculating prognoses. Challenges of prognostication identified include the following: (i) the area under the curve (a value that represents how well a model can distinguish between two outcomes) cannot be directly interpreted clinically and (ii) difficulties exist related to determining appropriate thresholds of accuracy in this context.

CONCLUSIONS

Prognostication is a significant aspect of VAD, and the utility of the currently available prognostic tools appears limited but may prompt discussions about prognosis and alternative means (other than prognostic estimates) to identify those eligible for VAD.

Multimodal survival prediction in advanced pancreatic cancer using machine learning

Background

Existing risk scores appear insufficient to assess the individual survival risk of patients with advanced pancreatic ductal adenocarcinoma (PDAC) and do not take advantage of the variety of parameters that are collected during clinical care.

Methods

In this retrospective study, we built a random survival forest model from clinical data of 203 patients with advanced PDAC. The parameters were assessed before initiation of systemic treatment and included age, CA19-9, C-reactive protein, metastatic status, neutrophil-to-lymphocyte ratio and total serum protein level. Separate models including imaging and molecular parameters were built for subgroups.

Results

Over the entire cohort, a model based on clinical parameters achieved a c-index of 0.71. Our approach outperformed the American Joint Committee on Cancer (AJCC) staging system and the modified Glasgow Prognostic Score (mGPS) in the identification of high- and low-risk subgroups. Inclusion of the KRAS p.G12D mutational status could further improve the prediction, whereas radiomics data of the primary tumor only showed little benefit. In an external validation cohort of PDAC patients with liver metastases, our model achieved a c-index of 0.67 (mGPS: 0.59).

Conclusions

The combination of multimodal data and machine-learning algorithms holds potential for personalized prognostication in advanced PDAC already at diagnosis.

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We developed a machine-learning-based prediction model that outperforms the AJCC staging system and mGPS.

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Applying our model to an external validation cohort demonstrates generalizability.

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Explainable machine learning enables to understand the decision making of our model and identifies relevant parameters.

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Combining clinical, imaging and genetic data holds potential for personalized prognostication in advanced PDAC.

Prognostic nomogram for patients with unresectable pancreatic cancer treated with gemcitabine plus nab-paclitaxel or FOLFIRINOX: A post-hoc analysis of a multicenter retrospective study in Japan (NAPOLEON study)

Background

No reliable nomogram has been developed until date for predicting the survival in patients with unresectable pancreatic cancer undergoing treatment with gemcitabine plus nab–paclitaxel (GnP) or FOLFIRINOX.

Methods

This analysis was conducted using clinical data of Japanese patients with unresectable pancreatic cancer undergoing GnP or FOLFIRINOX treatment obtained from a multicenter study (NAPOLEON study). A Cox proportional hazards model was used to identify the independent prognostic factors. A nomogram to predict 6–, 12–, and 18–month survival probabilities was generated, validated by using the concordance index (C–index), and calibrated by the bootstrapping method. And then, we attempted risk stratification for survival by classifying the patients according to the sum of the scores on the nomogram (total nomogram points).

Results

A total of 318 patients were enrolled. A prognostic nomogram was generated using data on the Eastern Cooperative Oncology Group performance status, liver metastasis, serum LDH, serum CRP, and serum CA19–9. The C–indexes of the nomogram were 0.77, 0.72 and 0.70 for 6–, 12–, and 18–month survival, respectively. The calibration plot showed optimal agreement at all points. Risk stratification based on tertiles of the total nomogram points yielded clear separations of the survival curves. The median survival times in the low–, moderate–, and high–risk groups were 15.8, 12.8 and 7.8 months (P<0.05), respectively.

Conclusions

Our nomogram might be a convenient and inexpensive tool to accurately predict survival in Japanese patients with unresectable pancreatic cancer undergoing treatment with GnP or FOLFIRINOX, and will help clinicians in selecting appropriate therapeutic strategies for individualized management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09139-y.

Nomogram to predict survival of patients with advanced and metastatic pancreatic Cancer

BACKGROUND

Nomograms are rarely employed to estimate the survival of patients with advanced and metastatic pancreatic cancer (PC). Herein, we developed a comprehensive approach to using a nomogram to predict survival probability in patients with advanced and metastatic PC.

METHODS

A total of 323 patients with advanced and metastatic PC were identified from the Chinese People's Liberation Army (PLA) General Hospital. A baseline nomogram was constructed using baseline variables of 323 patients. Additionally, 233 patients, whose tumors showed initial responses to first-line chemotherapy, were enrolled in the chemotherapy response-based model. 128 patients and 108 patients with advanced and metastatic PC from January 2019 to April 2021 were selected for external validating baseline model and chemotherapy response-based model. The 1-year and 2-year survival probability was evaluated using multivariate COX regression models. The discrimination and calibration capacity of the nomograms were assessed using C-statistic and calibration plots. The predictive accuracy and net benefit of the nomograms were evaluated using ROC curve and DCA, respectively.

RESULTS

In the baseline model, six variables (gender, KPS, baseline TB, baseline N, baseline WBC and baseline CA19-9) were used in the final model. In the chemotherapy response-based model, nine variables (KPS, gender, ascites, baseline N, baseline CA 19-9, baseline CEA, change in CA 19-9 level at week, change in CEA level at week and initial response to chemotherapy) were included in the final model. The C-statistics of the baseline nomogram and the chemotherapy response-based nomogram were 0.67 (95% CI, 0.62-0.71) and 0.74 (95% CI, 0.69-0.77), respectively.

CONCLUSION

These nomograms were constructed to predict the survival probability of patients of advanced and metastatic PC. The baseline model and chemotherapy response-based model performed well in survival prediction.

FOLFIRINOX relative dose intensity and disease control in advanced pancreatic adenocarcinoma

Background:

Most patients with advanced pancreatic adenocarcinoma (PA) treated with FOLFIRINOX experience adverse events requiring dose reduction. We aimed to assess the association between relative dose intensity (RDI) and disease control in a European setting.

Methods:

We retrospectively included patients with advanced PA treated with three or more cycles of FOLFIRINOX between 2011 and 2018 in six French centers. We computed the cumulative single-agent RDI (csRDI) before the first reassessment for each FOLFIRINOX agent (oxaliplatin, irinotecan, 5FU bolus, and 5FU intravenous infusion) and the cumulative multi-drug RDI (cmRDI) of their combination. The association between RDI and disease control or objective response at first reassessment was evaluated using multivariable logistic regression models controlling for performance status, liver metastases, and center.

Results:

We included 243 patients. Median csRDIs were 81%, 79%, 75%, and 85% for oxaliplatin, irinotecan, 5FU bolus, and 5FU intravenous infusion, respectively. Median cmRDI was 80%. None of the RDIs was significantly associated with disease control or objective response. Including RDI in a clinical model did not improve its ability to predict disease control; the area under the curve was 0.79 (95% CI: 0.73–0.85) with RDI versus 0.78 (95% CI: 0.72–0.85) without. Similar results were observed for the objective response.

Conclusion:

Pragmatic dose adjustments of FOLFIRINOX should be made by oncologists without considering a loss of effect.

Recent Discoveries of Diagnostic, Prognostic and Predictive Biomarkers for Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with a dismal prognosis that is frequently diagnosed at an advanced stage. Although less common than other malignant diseases, it currently ranks as the fourth most common cause of cancer-related death in the European Union with a five-year survival rate of below 9%. Surgical resection, followed by adjuvant chemotherapy, remains the only potentially curative treatment but only a minority of patients is diagnosed with locally resectable, non-metastatic disease. Patients with advanced disease are treated with chemotherapy but high rates of treatment resistance and unfavorable side-effect profiles of some of the used regimens remain major challenges. Biomarkers reflect pathophysiological or physiological processes linked to a disease and can be used as diagnostic, prognostic and predictive tools. Thus, accurate biomarkers can allow for better patient stratification and guide therapy choices. Currently, the only broadly used biomarker for PDAC, CA 19-9, has multiple limitations and the need for novel biomarkers is urgent. In this review, we highlight the current situation, recent discoveries and developments in the field of biomarkers of PDAC and their potential clinical applications.

Reactive oxygen species mediate anlotinib-induced apoptosis via activation of endoplasmic reticulum stress in pancreatic cancer

Anlotinib (AL3818), a novel multi-targeted receptor tyrosine kinase inhibitor, has recently been proven to be an antitumour drug. This study aimed to explore the antitumour effect of anlotinib and its underlying molecular mechanisms in human pancreatic cancer (PC) cells. The anti-proliferative effect of anlotinib for three PC cell lines was validated using CCK-8, colony formation and EdU detection assays. Cell cycle, cell apoptosis, and reactive oxygen species (ROS) detection assays, a PC xenograft model and immunohistochemistry were performed to elucidate the mechanisms by which anlotinib induced tumour lethality in vitro and in vivo. These results demonstrated that anlotinib inhibited proliferation, induced G2/M phase arrest and triggered apoptosis in PC cell lines. Anlotinib induced PC’s apoptosis through the accumulation of ROS which activated the endoplasmic reticulum (ER) stress via PERK/p-eIF2α/ATF4 pathway. Furthermore, we demonstrated that the expression level of Nrf2, an antioxidant protein, increased with anlotinib treatment. Nrf2 knockdown enhanced the pro-apoptotic effect of anlotinib and the expression of the PERK/p-eIF2α/ATF4 pathway. The in vivo results suggested that suppressing Nrf2 improved the antitumour effect of anlotinib on PC cells. These data indicated that the apoptotic effect of anlotinib on PC cells was induced by ER stress via the accumulation of ROS. In the future, anlotinib combined with an Nrf2 inhibitor may provide a new therapeutic strategy for the treatment of human PC.

Fluorouracil Supplemented With Oxaliplatin or Irinotecan for Solid Tumors: Indications From Clinical Characteristics and Health Outcomes of Patients

Fluorouracil combined with oxaliplatin (FOLFOX) and fluorouracil combined with irinotecan (FOLFIRI) are both first-line clinical chemotherapy regimens. However, clinicians' selection of FOLFIRI or FOLFOX medication regimens and their effects on patients' health outcomes are not clear. The aim of this study was to evaluate the impacts on patient characteristics of FOLFIRI or FOLFOX medication regimen selection and the effects of each regimen on patients' health outcomes in a real-world setting. Three thousand seven hundred and twenty-five patients were retrieved and 610 of them were eventually included in this study based on the inclusion and exclusion criteria. The percentages of the TNM stage, cetuximab, bevacizumab, and tumor metastases between the FOLFIRI and FOLFOX groups were different (P < 0.001). In the multivariate Cox proportional hazards model, a significantly higher non-convalescent incidence of the FOLFOX group was found as compared with the FOLFIRI group (HR = 2.211, 95% CI = 1.257–3.888, P = 0.006). In conclusion, the TNM stage, whether combined with cetuximab or bevacizumab, and whether there was tumor metastasis presented as the key factors affecting medication selection between the FOLFIRI and FOLFOX regimens. The FOLFIRI regimen exhibited better effects on patients' long-term health outcomes than did the FOLFOX regimen. This study was registered on the World Health Organization International Clinical Trials Registry Platform (ChiCTR2000029201).

Trial registration: ChiCTR2000029201.

First-line gemcitabine plus nab-paclitaxel for elderly patients with metastatic pancreatic cancer: Crossing the frontier of age?

BACKGROUND

Gemcitabine plus nab-paclitaxel (Gem-Nab) represents a standard first-line treatment for metastatic pancreatic cancer (mPC), but few data are available for elderly patients. We aimed to add evidence about safety and efficacy of Gem-Nab in this population.

METHODS

We collected data of 156 patients with mPC aged ≥65 years receiving Gem-Nab. Patients were stratified according to age: <70 (group 1: 65 patients) and ≥70 years (group 2: 91 patients).

RESULTS

The median age was 71 years (range: 65-87 years). The toxicity profile was similar between group 1 and 2, except for all-grade anaemia (92.1% vs. 78.7%, respectively; p = 0.04) and neurotoxicity (61.9% vs. 40.4%, respectively; p = 0.02), also as a result of a lower dose intensity of nab-paclitaxel (83.3% vs. 90.5%, respectively; p = 0.04) administered to oldest patients. The response rate was 25.6% (group 1 vs. 2: 20.0% vs. 29.7%; p = 0.12). After a median follow-up of 26.5 months, median overall survival (OS) and progression-free survival (PFS) were similar between the groups (p > 0.05). The starting dose of Gem-Nab did not affect PFS and OS (p > 0.05).

CONCLUSION

Gem-Nab is active and effective in older patients with mPC, with the results in line with the general mPC population enrolled in clinical trials. Mild dose modifications for elderly patients might be considered to improve safety without impairing efficacy.