Pemetrexed plus carboplatin versus pemetrexed in pretreated patients with advanced non-squamous non-small-cell lung cancer: treating the right patients based on individualized treatment effect prediction

Inflammatory risk stratification individualizes anti-inflammatory pharmacotherapy for acute type A aortic dissection

The systemic benefits of anti-inflammatory pharmacotherapy vary across cardiovascular diseases in clinical practice. We aimed to evaluate the application of artificial intelligence to acute type A aortic dissection (ATAAD) patients to determine the optimal target population who would benefit from urinary trypsin inhibitor use (ulinastatin). Patient characteristics at admission in the Chinese multicenter 5A study database (2016-2022) were used to develop an inflammatory risk model to predict multiple organ dysfunction syndrome (MODS). The population (5,126 patients from 15 hospitals) was divided into a 60% sample for model derivation, with the remaining 40% used for model validation. Next, we trained an extreme gradient-boosting algorithm (XGBoost) to develop a parsimonious patient-level inflammatory risk model for predicting MODS. Finally, a top-six-feature tool consisting of estimated glomerular filtration rate, leukocyte count, platelet count, De Ritis ratio, hemoglobin, and albumin was built and showed adequate predictive performance regarding its discrimination, calibration, and clinical utility in derivation and validation cohorts. By individual risk probability and treatment effect, our analysis identified individuals with differential benefit from ulinastatin use (risk ratio [RR] for MODS of RR 0.802 [95% confidence interval (CI) 0.656, 0.981] for the predicted risk of 23.5%-41.6%; RR 1.196 [0.698-2.049] for the predicted risk of <23.5%; RR 0.922 [95% CI 0.816-1.042] for the predicted risk of >41.6%). By using artificial intelligence to define an individual's benefit based on the risk probability and treatment effect prediction, we found that individual differences in risk probability likely have important effects on ulinastatin treatment and outcome, which highlights the need for individualizing the selection of optimal anti-inflammatory treatment goals for ATAAD patients.

Personalizing treatment in end-stage kidney disease: deciding between hemodiafiltration and hemodialysis based on individualized treatment effect prediction

Background

Previous studies suggest that hemodiafiltration reduces mortality compared to hemodialysis in patients with end-stage kidney disease (ESKD), but controversy surrounding its benefits remain and it is unclear to what extent individual patients benefit from hemodiafiltration. This study aimed to develop and validate a treatment effect prediction model to determine which patients would benefit most from hemodiafiltration compared to hemodialysis in terms of all-cause mortality.

Methods

Individual participant data from four randomized controlled trials comparing hemodiafiltration with hemodialysis on mortality were used to derive a Royston-Parmar model for prediction of absolute treatment effect of hemodiafiltration based on pre-specified patient and disease characteristics. Validation of the model was performed using internal-external cross validation.

Results

The median predicted survival benefit was 44 (Q1-Q3: 44–46) days for every year of treatment with hemodiafiltration compared to hemodialysis. The median survival benefit with hemodiafiltration ranged from 2 to 48 months. Patients who benefited most from hemodiafiltration were younger, less likely to have diabetes or a cardiovascular history and had higher serum creatinine and albumin levels. Internal-external cross validation showed adequate discrimination and calibration.

Conclusion

Although overall mortality is reduced by hemodiafiltration compared to hemodialysis in ESKD patients, the absolute survival benefit can vary greatly between individuals. Our results indicate that the effects of hemodiafiltration on survival can be predicted using a combination of readily available patient and disease characteristics, which could guide shared decision-making.

Treatment Sequencing Strategies in Lung Cancer

BACKGROUND

The advances in the lung cancer screening methods and therapeutics, together with awareness towards deleterious habits, such as smoking, is increasing the overall survival with better quality of life for the patients. However, lung cancer is still one of the most common and fatal neoplasm with a high incidence and consequently burden to public health worldwide. Thus, based on guidelines and recent phases II and III clinical trials studies, this manuscript summarizes the current treatment sequencing strategies in lung cancer.

METHODS

A comprehensive search of related articles was performed focused on phases II and III clinical trials studies.

RESULTS

The lung cancer management should take into consideration the tumor characteristics, histology, molecular pathology and be discussed in a multidisciplinary team. Lung cancer treatment options comprises surgery whenever possible, radiotherapy associate with/or chemotherapy and immunotherapy as monotherapy, or combined with chemotherapy and best palliative care.

CONCLUSIONS

The screening predictability in more patients, smoking reduction, early diagnosis, better disease understanding and individualized, more effective and tolerable therapeutics are related to an increasing in overall survival and quality of life. In the near future improvement of personalized therapy in precision medicine is expected, enhancing new predictive biomarkers, optimal doses and optimal treatment sequencing as well as anti-cancer vaccines development.

Prediction of response to pemetrexed in non-small-cell lung cancer with immunohistochemical phenotyping based on gene expression profiles

Background

Palliative pemetrexed-based chemotherapy remains a standard of care treatment for the majority of patients with advanced non-squamous non-small-cell lung cancer (NSCLC). Currently, no predictive markers for pemetrexed treatment are available.

Methods

Resected tumour samples from pemetrexed-naïve NSCLC patients were collected. Gene expression profiling with respect to predicted sensitivity to pemetrexed classified predicted responders (60%) and non-responders (40%) based on differentially expressed genes encoding for pemetrexed target enzymes. Genes showing a strong correlation with these target genes were selected for measurement of corresponding protein expressions by immunohistochemical (IHC) staining. A semi-quantitative IHC scoring method was applied to construct a prediction model for response to pemetrexed. A retrospective cohort of patients with advanced NSCLC treated with first-line pemetrexed-based chemotherapy was used for external validation.

Results

From ninety-one patients resected tumour samples were collected. The majority of patients had early or locally advanced NSCLC (96.3%). Gene expression profiling revealed five markers, which mRNA levels strongly correlated to pemetrexed target genes mRNA levels: TPX2, CPA3, EZH2, MCM2 and TOP2A. Of 63 (69%) patients IHC staining scores of these markers were obtained, which significantly differed between predicted non-responders and responders (P < 0.05). The optimized prediction model included EZH2 (OR = 0.56, 95% CI 0.35–0.90) and TPX2 (OR = 0.55, 95% CI 0.30–1.01). The model had a sensitivity of 86.8%, specificity of 63.6% and showed a good ability to distinct between responders and non-responders (C-index 0.86).

In the external study population (N = 23) the majority of patients had metastatic NSCLC (95.7%). Partial response (PR) was established in 26.1%. The sensitivity decreased drastically to 33.3%, with a specificity of 82.4% and a C-index of 0.73.

Conclusions

Using external validation this prediction model with IHC staining of target enzyme correlated markers showed a good discrimination, but lacked sensitivity. The role of IHC markers as response predictors for pemetrexed in clinical practice remains questionable.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5645-x) contains supplementary material, which is available to authorized users.

Choosing the right strategy based on individualized treatment effect predictions: combination versus sequential chemotherapy in patients with metastatic colorectal cancer

BACKGROUND

Translating results from randomized trials to individual patients is challenging, since treatment effects may vary due to heterogeneous prognostic characteristics. We aimed to demonstrate model development for individualized treatment effect predictions in cancer patients. We used data from two randomized trials that investigated sequential versus combination chemotherapy in unresectable metastatic colorectal cancer (mCRC) patients.

MATERIAL AND METHODS

We used data from 803 patients included in CAIRO for prediction model development and internal validation, and data from 1423 patients included in FOCUS for external validation. A Weibull model with pre-specified patient and tumour characteristics was developed for a prediction of gain in median overall survival (OS) by upfront combination versus sequential chemotherapy. Decision curve analysis with net benefit was used. A nomogram was built using logistic regression for estimating the probability of receiving second-line treatment after the first-line monochemotherapy.

RESULTS

Median-predicted gain in OS for the combination versus sequential chemotherapy was 2.3 months (IQR: -1.1 to 3.7 months). A predicted gain in favour of sequential chemotherapy was found in 231 patients (29%) and a predicted gain of >3 months for combination chemotherapy in 294 patients (37%). Patients with benefit from sequential chemotherapy had metachronous metastatic disease and a left-sided primary tumour. Decision curve analyses showed improvement in a net benefit for treating all patients according to prediction-based treatment compared to treating all patients with combination chemotherapy. Multiple characteristics were identified as prognostic variables which identify patients at risk of never receiving second-line treatment if treated with initial monochemotherapy. External validation showed good calibration with moderate discrimination in both models (C-index 0.66 and 0.65, respectively).

CONCLUSIONS

We successfully developed individualized prediction models including prognostic characteristics derived from randomized trials to estimate treatment effects in mCRC patients. In times where the heterogeneity of CRC becomes increasingly evident, such tools are an important step towards personalized treatment.