# An Investigation of the Molecular Mechanisms for and Prediction of the Severity of Cancer Chemotherapy-Related Fatigue Using a Multi-staged Integrated Omics Approach

> **NIH NIH R37** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $634,342

## Abstract

Cancer-related fatigue (CRF) is the most common symptom associated with cancer and its treatments.
Moderate to severe CRF has a negative impact on patients’ ability to tolerate treatments as well as on their
quality of life. In some patients, CRF is so severe, that they discontinue cancer treatment. Given its high
occurrence and significant negative impact, it is imperative that effective treatments be developed for this
devastating symptom. Two of the major knowledge gaps for CRF are a lack of a risk prediction model and a
lack of knowledge of its underlying mechanisms. A sensitive and specific risk prediction model would assist
clinicians to determine which patients are most likely to experience high levels of CRF and provide
recommendations regarding activity modifying interventions (e.g., exercise). Increased knowledge of the
mechanisms for CRF could identify potential targets for therapeutic interventions. Both of these knowledge
gaps will be addressed in this application. This study will use multiple sources of “omics” data to investigate
the molecular mechanisms associated with the severity of CRF in a well characterized sample of oncology
patients (n=1343) who are experiencing low versus high levels of morning and evening CRF. Because these
patients are undergoing chemotherapy (CTX), our study will investigate CTX-related fatigue (CTXRF). We will
use a multi-staged analysis to integrate the gene expression, genetic, and epigenetic data. We will take
advantage of the functional candidate genes identified in a gene expression profiling analyses to provide loci
for analysis in subsequent genetic and epigenetic analyses. Candidate genes and pathways identified in this
study will provide new and needed information on CTXRF mechanisms, as well as potential therapeutic
targets. Prior studies suggest that patients will experience an increase in the severity of CTXRF in the week
following CTX. However, no models exist to predict the magnitude of this increase. This inability to predict the
severity of CTXRF during subsequent cycles of CTX limits the ability of clinicians to identify high-risk patients
and provide them with recommendations to manage CTXRF. To address this knowledge gap, we propose to
use demographic, clinical, and omics data to develop a model to predict the severity of morning and evening
CTXRF experienced by a patient one week following CTX based on their profile for CTXRF in the week prior to
the receipt of this cycle of CTX. This study will provide new insights to be able to identify high-risk patients as
well as identify potential therapeutic targets. This project will guide the development and clinical studies to
investigate additional mechanisms and therapeutic interventions for CTXRF and other types of fatigue
associated with cancer and its treatment (e.g., radiation therapy, surgery).

## Key facts

- **NIH application ID:** 10657397
- **Project number:** 5R37CA233774-05
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Kord Michael Kober
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $634,342
- **Award type:** 5
- **Project period:** 2019-07-03 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10657397

## Citation

> US National Institutes of Health, RePORTER application 10657397, An Investigation of the Molecular Mechanisms for and Prediction of the Severity of Cancer Chemotherapy-Related Fatigue Using a Multi-staged Integrated Omics Approach (5R37CA233774-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10657397. Licensed CC0.

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