Machine learning and biological validation identify sphingolipids as key mediators of paclitaxel-induced neuropathy in cancer patients

  1. Institute of Clinical Pharmacology, Goethe - University, Theodor Stern Kai 7, 60590 Frankfurt am Main, Germany
  2. Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany
  3. Goethe University, Department of Gynecology and Obstetrics, Theodor-Stern-Kai 7, Frankfurt/Main, Germany
  4. German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
  5. Goethe University, University Cancer Center Frankfurt (UCT), Goethe University Hospital, Frankfurt am Main 60590, Germany
  6. Oncology Center, Sana-Klinikum Offenbach, Starkenburgring 66, 63069 Offenbach


  • Reviewing Editor
    Pramod Mistry
    Yale University, New Haven, United States of America
  • Senior Editor
    Pramod Mistry
    Yale University, New Haven, United States of America

Reviewer #1 (Public Review):


This study examines lipid profiles in cancer patients treated with the neurotoxic chemotherapy paclitaxel. Multiple methods, including machine learning as well as more conventional statistical modelling, were used to classify lipid patterns before and after paclitaxel treatment and in conjunction with neuropathy status. Lipid profiles before and after paclitaxel therapy were analysed from 31 patients. The study aimed to characterize from the lipid profile if plasma samples were collected pre-paclitaxel or post-paclitaxel and their relevance to neuropathy status. Sphingolipids including sphinganine-1-phosphate (SA1P) differed between patients with and without neuropathy. To examine the potential role of SA1P, it was applied to murine primary sensory neuron cultures, and produced calcium transients in a proportion of neurons. This response was abolished by the application of a TRPV1 antagonist. The number of neurons responding to SA1P was partially reduced by the sphingosine 1-phosphate receptor (S1PR1) modulator fingolimod.


The strengths of this study include the use of multiple methods to classify lipid patterns and the attempt to validate findings from the clinical cohort in a preclinical model using primary sensory neurons.


There are a number of weaknesses in the study. The small sample size is a significant limitation of the study. Out of 31 patients, only 17 patients were reported to develop neuropathy, with significant neuropathy (grade 2/3) in only 5 patients. The authors acknowledge this limitation in the results and discussion sections of the manuscript, but it limits the interpretation of the results. Also acknowledged is the limited method used to assess neuropathy.

Potentially due to this small number of patients with neuropathy, the machine learning algorithms could not distinguish between samples with and without neuropathy. Only selected univariate analyses identified differences in lipid profiles potentially related to neuropathy.

Three sphingolipid mediators including SA1P differed between patients with and without neuropathy at the end of treatment. These sphingolipids were elevated at the end of treatment in the cohort with neuropathy, relative to those without neuropathy. However, across all samples from pre to post-paclitaxel treatment, there was a significant reduction in SA1P levels. It is unclear from the data presented what the underlying mechanism for this result would be. If elevated SA1P is associated with neuropathy development, it would be expected to increase in those who develop neuropathy from pre to post-treatment time points.

Primary sensory neuron cultures were used to examine the effects of SA1P application. SA1P application produced calcium transients in a small proportion of sensory neurons. It is not clear how this experimental model assists in validating the role of SA1P in neuropathy development as there is no assessment of sensory neuron damage or other hallmarks of peripheral neuropathy. These results demonstrate that some sensory neurons respond to SA1P and that this activity is linked to TRPV1 receptors. However, further studies will be required to determine if this is mechanistically related to neuropathy.


Taken in total, the data presented do not provide sufficient evidence to support the contention that SA1P has an important role in paclitaxel-induced peripheral neuropathy. Further, the results do not provide evidence to support the use of S1PR1 receptor antagonists as a therapeutic strategy. It is important to be careful with language use in the discussion, as the significance of the present results is overstated.

However, based on the results of previous studies, it is likely that sphingolipid metabolism plays a role in chemotherapy-induced peripheral neuropathy. Based on this existing evidence, the S1PR1 receptor antagonist fingolimod has already been examined in experimental models and clinical trials. Further work is needed to examine the links between lipid mediators and neuropathy development and identify additional strategies for intervention.

Reviewer #2 (Public Review):


The study investigates the mechanisms underlying chemotherapy-induced peripheral neuropathy (CIPN), a notable side effect of commonly used anticancer drugs like paclitaxel. It aims to comprehend the putative mechanisms through lipidomics analysis of plasma samples from cancer patients pre and post-paclitaxel treatment, drawing inspiration from preclinical studies highlighting the role of sphingolipids. While the use of patient plasma samples stands out as a major strength, shortcomings in the result presentation undermine the study's significance. The introduction lacks a robust rationale, failing to articulate the utility of machine learning methods over conventional lipidomics analysis and the relevance of broader neuropathy in the context of the study's goal of investigating peripheral neuropathy. The failure to robustly link neuropathy to paclitaxel treatment, with only around 50% of patients developing neuropathy, mostly at Grade 1, with no or mild symptoms that require no intervention, weakens the study's impact. The presentation of results lacks clarity on sphingolipid dysregulation, leaving uncertainty regarding downregulation or upregulation. Furthermore, no clarity in validation for the machine learning-based analysis with conventional methods and an overall weakness in result representation weaken the study, despite addressing an important question in the field.


The study leverages patient plasma samples before and after paclitaxel treatment, enhancing the translatability of findings to patient impact. The attempt to employ machine learning (ML) methods for analyzing biological samples and classifying patient groups is commendable, pushing the biomedical sciences towards ML applications for handling complex data. The chosen topic of investigating chemotherapy-induced peripheral neuropathy (CIPN) is clinically important, offering potential benefits for cancer patients undergoing chemotherapy treatment.


The article is poorly written, hindering a clear understanding of core results. While the study's goals are apparent, the interpretation of sphingolipids, particularly SA1P, as key mediators of paclitaxel-induced neuropathy lacks robust evidence. The introduction fails to establish the significance of general neuropathy or peripheral neuropathy in anticancer drug-treated patients, and crucial details, such as the percentage of patients developing general neuropathy or peripheral neuropathy, are omitted. This omission is particularly relevant given that only around 50% of patients developed neuropathy in this study, primarily of mild Grade 1 severity with negligible symptoms, contradicting the study's assertion of CIPN as a significant side effect. The lack of clarity in distinguishing results obtained by lipidomics using machine learning methods and conventional methods adds to the confusion. The poorly written results section fails to specify SA1P's downregulation or upregulation, and the process of narrowing down to sphingolipids and SA1P is inadequately explained. Integrating a significant portion of the discussion section into the results section could enhance clarity. An explanation of the utility of machine learning in classifying patient groups over conventional methods and the citation of original research articles, rather than relying on review articles, may also add clarity to the usefulness of the study.

  1. Howard Hughes Medical Institute
  2. Wellcome Trust
  3. Max-Planck-Gesellschaft
  4. Knut and Alice Wallenberg Foundation