Patient-Reported Outcomes as Interradiographic Predictors of Response in Non-Small Cell Lung Cancer

Predicting resistance and pseudoprogression: are minimalistic immunoediting mathematical models capable of forecasting checkpoint inhibitor treatment outcomes in lung cancer?

BACKGROUND

The increased application of immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 in lung cancer treatment generates clinical need to reliably predict individual patients' treatment outcomes.

METHODS

To bridge the prediction gap, we examine four different mathematical models in the form of ordinary differential equations, including a novel delayed response model. We rigorously evaluate their individual and combined predictive capabilities with regard to the patients' progressive disease (PD) status through equal weighting of model-derived outcome probabilities.

RESULTS

Fitting the complete treatment course, the novel delayed response model (R2=0.938) outperformed the simplest model (R2=0.865). The model combination was able to reliably predict patient PD outcome with an overall accuracy of 77% (sensitivity = 70%, specificity = 81%), solely through calibration with primary tumor longest diameter measurements. It autonomously identified a subset of 51% of patients where predictions with an overall accuracy of 81% (sensitivity = 81%, specificity = 81%) can be achieved. All models significantly outperformed a fully data-driven machine learning-based approach.

IMPLICATIONS

These modeling approaches provide a dynamic baseline framework to support clinicians in treatment decisions by identifying different treatment outcome trajectories with already clinically available measurement data.

LIMITATIONS AND FUTURE DIRECTIONS

Conjoint application of the presented approach with other predictive tools and biomarkers, as well as further disease information (e.g. metastatic stage), could further enhance treatment outcome prediction. We believe the simple model formulations allow widespread adoption of the developed models to other cancer types. Similar models can easily be formulated for other treatment modalities.

Defining and Addressing Research Priorities in Cancer Cachexia through Transdisciplinary Collaboration

For many patients, the cancer continuum includes a syndrome known as cancer-associated cachexia (CAC), which encompasses the unintended loss of body weight and muscle mass, and is often associated with fat loss, decreased appetite, lower tolerance and poorer response to treatment, poor quality of life, and reduced survival. Unfortunately, there are no effective therapeutic interventions to completely reverse cancer cachexia and no FDA-approved pharmacologic agents; hence, new approaches are urgently needed. In May of 2022, researchers and clinicians from Moffitt Cancer Center held an inaugural retreat on CAC that aimed to review the state of the science, identify knowledge gaps and research priorities, and foster transdisciplinary collaborative research projects. This review summarizes research priorities that emerged from the retreat, examples of ongoing collaborations, and opportunities to move science forward. The highest priorities identified include the need to (1) evaluate patient-reported outcome (PRO) measures obtained in clinical practice and assess their use in improving CAC-related outcomes; (2) identify biomarkers (imaging, molecular, and/or behavioral) and novel analytic approaches to accurately predict the early onset of CAC and its progression; and (3) develop and test interventions (pharmacologic, nutritional, exercise-based, and through mathematical modeling) to prevent CAC progression and improve associated symptoms and outcomes.

Under-reporting of subjective symptoms and its prognostic value: a pooled analysis of 12 cancer clinical trials

BACKGROUND

Oncologists tend to under-report subjective symptoms during cancer treatment. This study describes the under-reporting rate of selected symptoms and explores its association with overall survival (OS). A secondary aim is to test the association of patient-reported symptoms with OS.

PATIENTS AND METHODS

This is a post hoc analysis on data pooled from 12 randomized trials, promoted by the National Cancer Institute of Naples (Italy), enrolling patients between 2002 and 2019, with published primary analyses. Occurrence and grade of six side-effects (anorexia, nausea, vomiting, constipation, diarrhea and fatigue) reported by physicians were compared with corresponding symptoms reported by patients in quality-of-life (QoL) questionnaires. Under-reporting was defined as the rate of cases reported grade 0 by the physician while grade ≥1 by the patient. Prognostic value was tested in a multivariable model stratified by trial, including age, sex and performance status as confounders. A landmark threshold was defined for OS analyses.

RESULTS

3792 patients with advanced lung, ovarian, pancreatic, breast or colorectal cancer were pooled; 2603 (68.6%) were eligible having at least one toxicity assessment and one QoL questionnaire, before the first planned disease restaging. Concordance between physicians' and patients' reporting was low with Cohen's k coefficients ranging from 0.03 (fatigue) to 0.33 (vomiting). Under-reporting ranged from 52.7% (nausea) to 80.5% (anorexia), and was not associated with OS. Patient-reported anorexia, vomiting and fatigue ('a little' or more) were significantly associated with shorter OS.

CONCLUSIONS

Under-reporting of treatment side-effects is frequent, but it does not affect OS. Patients' reported symptoms should be used for prognostic evaluation.