Breakthrough Cancer Pain Clinical Features and Differential Opioids Response: A Machine Learning Approach in Patients With Cancer From the IOPS-MS Study

PURPOSE A large proportion of patients with cancer suffer from breakthrough cancer pain (BTcP). Several unmet clinical needs concerning BTcP treatment, such as optimal opioid dosages, are being investigated. In this analysis the hypothesis, we explore with an unsupervised learning algorithm whether distinct subtypes of BTcP exist and whether they can provide new insights into clinical practice. METHODS Partitioning around a k-medoids algorithm on a large data set of patients with BTcP, previously collected by the Italian Oncologic Pain Survey group, was used to identify possible subgroups of BTcP. Resulting clusters were analyzed in terms of BTcP therapy satisfaction, clinical features, and use of basal pain and rapid-onset opioids. Opioid dosages were converted to a unique scale and the BTcP opioids-to-basal pain opioids ratio was calculated for each patient. We used polynomial logistic regression to catch nonlinear relationships between therapy satisfaction and opioid use. RESULTS Our algorithm identified 12 distinct BTcP clusters. Optimal BTcP opioids-to-basal pain opioids ratios differed across the clusters, ranging from 15% to 50%. The majority of clusters were linked to a peculiar association of certain drugs with therapy satisfaction or dissatisfaction. A free online tool was created for new patients’ cluster computation to validate these clusters in future studies and provide handy indications for personalized BTcP therapy. CONCLUSION This work proposes a classification for BTcP and identifies subgroups of patients with unique efficacy of different pain medications. This work supports the theory that the optimal dose of BTcP opioids depends on the dose of basal opioids and identifies novel values that are possibly useful for future trials. These results will allow us to target BTcP therapy on the basis of patient characteristics and to define a precision medicine strategy also for supportive care.

[1]  Yannig Goude,et al.  Scalable visualisation methods for modern Generalized Additive Models , 2018 .

[2]  A. Caraceni,et al.  Factors Influencing the Clinical Presentation of Breakthrough Pain in Cancer Patients , 2018, Cancers.

[3]  E. Zecca,et al.  Breakthrough Cancer Pain: Preliminary Data of The Italian Oncologic Pain Multisetting Multicentric Survey (IOPS-MS) , 2016, Advances in Therapy.

[4]  A. Caraceni,et al.  Breakthrough pain and its treatment: critical review and recommendations of IOPS (Italian Oncologic Pain Survey) expert group , 2016, Supportive Care in Cancer.

[5]  S. Barni,et al.  Italian Oncological Pain Survey (IOPS): A Multicentre Italian Study of Breakthrough Pain Performed in Different Settings , 2015, The Clinical journal of pain.

[6]  Pedro M. Valero-Mora,et al.  ggplot2: Elegant Graphics for Data Analysis , 2010 .

[7]  C. Reid,et al.  The management of cancer‐related breakthrough pain: Recommendations of a task group of the Science Committee of the Association for Palliative Medicine of Great Britain and Ireland , 2009, European journal of pain.

[8]  S. Wood Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models , 2004 .

[9]  E. Zecca,et al.  Breakthrough pain characteristics and syndromes in patients with cancer pain. An international survey , 2004, Palliative medicine.

[10]  R. Tibshirani,et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Paul Jacobsen,et al.  Breakthrough pain: characteristics and impact in patients with cancer pain , 1999, Pain.

[12]  R. Portenoy,et al.  Breakthrough pain: definition, prevalence and characteristics , 1990, Pain.

[13]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[14]  J. Gower A General Coefficient of Similarity and Some of Its Properties , 1971 .

[15]  Richard Carter,et al.  Breakthrough , 1966 .

[16]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[17]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .