Research

Molecular processes are carefully orchestrated and remarkably resilient to a wide variety of changes in the environment. 

Genetic variations encode inherited material (i.e., nature), epigenetic variations encode responses to the environment (i.e., nurture), and gene expression produces the functional gene products of these two processes.

We are interested in understanding the relative contribution of each of these processes singly and in combination to common symptoms (e.g., fatigue, neuropathy) or treatment failure (i.e., antiretroviral (ARV) therapy failure to treat HIV). 

We are proud of our track-record of successful collaborations and mentorships with transdisciplinary research teams. We believe this approach is needed to develop future symptom scientists who work with genomics and big data.

A selection of publications are featured below.


 

Molecular Mechanisms Underlying Cancer-related Symptoms (i.e., Fatigue and Neuropathy)

When molecular processes are perturbed, patient symptom severity can change. These symptoms can have a negative impact on patient's ability to tolerate treatments and on their quality of life. We are particularly interested in cancer-related fatigue (CRF) and chemotherapy-induced peripheral neuropathy (CIPN). The goals of this research are to identify mechanism contributing to cancer related symptoms and to identify patients who are at greatest risk for the most severe symptom burden (i.e., CRF, CIPN). From a phenotypic perspective, this work has focused on the use of novel statistical approaches (e.g., hierarchical linear modeling, latent class analysis) to identify patients who have the highest symptom burden. From a molecular perspective, our efforts are focused on the mechanisms that underlie these common symptoms in oncology patients and survivors, as well as the mechanisms that contribute to the high-risk symptom phenotype by applying bioinformatic and systems biology approaches using high-throughput genomics (i.e., genomics, epigenomics, transcriptomics, and metabolomics). 

Cancer- and Chemotherapy-Related Fatigue
  • Kober KM and Yom SS. Doc, I Feel Tired … Oh Really, So How’s Your Mucositis? Cancer. 2020. PMID 34028000.
  • Chou Y-J, Kober KM, Kuo C-H, Yeh K-H, Kuo T-C, Tseng YJ, Miaskowski C, Shun S-C. A Pilot Study of Metabolomic Pathways Associated With Fatigue in Survivors of Colorectal Cancer. Biol Res Nurs. 2020. PMID 32696652.
  • Wright F, Dunn LB, Paul SM, Conley YP, Levine JD, Hammer MJ, Cooper BA, Miaskowski C, Kober KM. Morning Fatigue Severity Profiles in Oncology Outpatients Receiving Chemotherapy. Cancer Nursing. 2019. PMC6336532.
  • Flowers E, Wright F, Cooper BA, Conley YP, Hammer MJ, Chen L-M, Paul SM, Levine JD, Miaskowski C, Kober KM. Differential expression of genes and differentially perturbed pathways associated with very high evening fatigue in oncology patients receiving chemotherapy. Support Care Cancer. 2018. PMC5786467.
  • Eshragh J, Dhruva A, Paul SM, Cooper BA, Mastick J, Hamolsky D, Levine JD, Miaskowski C, Kober KM. Associations between neurotransmitter genes and fatigue and energy levels in women after breast cancer surgery. J Pain Symptom Manage. 2017. PMC5191954.
  • Kober KM, Cooper BA, Paul SM, Dunn LB, Levine JD, Wright F, Hammer MJ, Mastick J, Venook A, Aouizerat BE, Miaskowski C. Subgroups of chemotherapy patients with distinct morning and evening fatigue trajectories. Support Care Cancer. 2016. PMC5473960.
  • Kober KM, Smoot B, Paul SM, Cooper BA, Levine JD, Miaskowski C. Polymorphisms in cytokine genes are associated with higher levels of fatigue and lower levels of energy in women after breast cancer surgery. J Pain Symptom Manage. 2016. PMC5107347.
Chemotherapy-Induced Peripheral Neuropathy
  • Kober KM, Lee M-C, Olshen A, Conley YP, Sirota M, Keiser M, Hammer MJ, Abrams G, Schumacher M, Levine JD, Miaskowski C. Differential methylation and expression of genes in the Hypoxia Inducible Factor 1 (HIF-1) signaling pathway are associated with Paclitaxel-induced peripheral neuropathy in breast cancer survivors and with preclinical models of chemotherapy-induced neuropathic pain. Mol Pain. 2020. PMC7322824
  • Kober KM, Schumacher M, Conley YP, Topp K, Mazor M, Hammer MJ, Paul SM, Levine JD, Miaskowski C. Signaling pathways and gene co-expression modules associated with cytoskeleton and axon morphology in breast cancer survivors with chronic Paclitaxel-induced peripheral neuropathy. Mol Pain. 2019. PMC6755139.
  • Miaskowski C, Topp K, Conley YP, Paul SM, Melisko M, Schumacher M, Chesney M, Abrams G, Levine JD, Kober KM. Perturbations in neuroinflammatory pathways are associated with Paclitaxel-induced peripheral neuropathy in breast cancer survivors. J Neuroimmunol. 2019. PMC6788784.
  • Kober KM, Mazor M, Abrams G, Olshen A, Conley YP, Hammer M, Schumacher M, Chesney M, Smoot B, Mastick J, Paul SM, Levine JD, Miaskowski C. Phenotypic characterization of Paclitaxel-induced peripheral neuropathy in cancer survivors. J Pain Symptom Manage. 2018. PMC6289693.
  • Kober KM, Olshen A, Conley YP, Schumacher M, Topp K, Smoot B, Mazor M, Chesney M, Hammer M, Paul SM, Levine JD, Miaskowski C. Expression of mitochondrial dysfunction-related genes and pathways in Paclitaxel-induced peripheral neuropathy in breast cancer survivors. Mol Pain. 2018. PMC6293373.

 


Prediction Models for Symptom Severity

Effective symptom management is a critical component of cancer treatment. Computational tools that predict the course and severity of these symptoms have the potential to assist oncology clinicians to personalize the patient’s treatment regimen more efficiently and provide more aggressive and timely interventions. Cancer-related fatigue (CRF) is the most common symptom associated with cancer and its treatments. CRF has a negative impact on the patients’ ability to tolerate treatments and on their quality of life.  One of the limitations to effective treatment of CRF is the availability of a valid and reliable model to predict the severity of CRF.  Our research aims to integrate demographic, clinical, and molecular data to develop accurate models to predict symptom severity over the course of chemotherapy treatment. This type of predictive model can be used to identify high risk patients, educate patients about their symptom experience, and improve the timing of pre-emptive and personalized symptom management interventions. We use machine learning approaches to develop and evaluate models predicting symptoms in oncology patients.

  • Kober KM, Roy R, Dhruva A, Conley YP, Chan RJ, Cooper B, Olshen A, Miaskowski C. Prediction of evening fatigue severity in outpatients receiving chemotherapy: less may be more. Fatigue: Biomedicine, Health & Behavior. 2021. DOI:10.1080/21641846.2021.1885119.
  • N Papachristou, D Puschmann, P Barnaghi, B Cooper, X Hu, Maguire R, Apostolidis K, Conley YP, Hammer M, Katsaragakis S, Kober KM, Levine JD, McCann L, Patiraki E, Furlong EP, Fox PA, Paul SM, Ream E, Wright F, Miaskowski C. Learning from data to predict future symptoms of oncology patients. PLOS ONE. 2018. PMC6312306.

 


 

Viral Genomics and HIV Disease Progression

Highly active antiretroviral treatment (HAART) is a highly effective treatment for HIV infection which aims to suppress viral replication. Despite its effectiveness, many patients experience virologic failure during HAART, where detectible viral loads are observed. Early virologic failure in the setting of HAART increases the risk of subsequent virological failure and disease progression. The presence of drug resistant mutations and suboptimal adherence are independent predictors of virologic failure. Genotypic resistance is one of the most important predictors of antiviral response and incomplete adherence is a strong predictor of treatment failure and viral rebound. Importantly, adherence early on in treatment is of critical importance as improved adherence has little impact on some treatments once the first mutations have emerged. Our research aims to reliably measure viral genome variation to quantify changes in the intra-patient HIV population over time by sampling each patient across time-points. These methods will be used in future studies investigating the evolution of HIV, including the identification of sites that influence virologic failure during HAART. Recently, we collaborated on a project evaluating if patterns of evolution in HIV-1 gag and env-gp120 were associated with biological sex differences.

  • MJ Dapp, KM Kober, L Chen, DH Westfall, K Wong, H Zhao, BM Hall, Deng W, Sibley T, Ghorai S, Kim K, Chen N, McHugh S, Au L, Cohen M, Anastos K, Mullins JI. Patterns and rates of viral evolution in HIV-1 subtype B infected females and males. PLOS ONE. 2017. PMC5646779

 


Molecular Evolution

This basic science research, initiated by my doctoral research and funded by an NSF Doctoral Dissertation Improvement Grant (DEB-1011061), is centered on the theoretical underpinnings of molecular evolutionary genetics through the use of full genome scale data (i.e., sequencing, assembly, annotations) and the sea urchin model system. Firstly, this work provided a comparative genomics dataset and phylogeny which serves as the basis for modern research using the sea urchin model system. Next, we found that mutational bias has played a minor role in determining codon bias in the sea urchin and that preferred codon usage may be heterogeneous across different genes and subject to different forms of natural selection. Finally, we confirmed the widespread action of positive selection across sea urchin genomes, and allowed us to reject the possibility that annotation and alignment errors (including paralogs) were responsible for creating false signals of adaptive molecular divergence. These findings have broad implications for interpreting the role of synonymous codon usage and molecular adaptation in other species, including humans, as well as our understanding of the mechanisms involved in human disease where natural selection is active in shaping the biological conditions (i.e., cancer, HIV infection).

 


 

Resources for Understanding and Utilizing High Throughput Genomic Data

The major advancements in sequencing technologies obtained over the past decade have resulted in the collection of enormous amounts of sequencing data from a variety of biological sources. With these new datasets, a large variety of research questions can now be addressed which previously were unattainable. However, many of these new research directions require a deep understanding the new technologies and their applications, the implementation of novel adaptations to existing tools, or the development of entirely new tools. As we explore new research space and develop new tools and resources for our own program of research, we share them with the broader research community with the hope that others may benefit from their usage in their own research projects.

  • Understanding the role of epigenetic changes in gene expression is a fundamental question of molecular biology. Predictions of gene expression values from epigenetic data have tremendous research and clinical potential. Mentored by Eric Mjolsness and Timothy Downing at UC Irvine, Jim Brunner (Mayo Clinic), Jacob Kim (Columbia University) and I developed stoch_epi_lib, a novel stochastic dynamical systems model that predicts gene expression levels from methylation data of genes in a given gene regulator network. A preprint of the article is available at bioRxiv Systems Biology and at arXiv Molecular Networks. The software and usage instructions are publicly available online at: https://github.com/kordk/stoch_epi_lib
  • In support of the research community’s response to the COVID-19 pandemic, from March through June 2020 Kiley Charbonneau and Maureen Lewis from the lab have been performing literature reviews and providing annotations of human and viral genome mappable data for the SARS-CoV-2 Genome Browser for the Crowd-Sourced Data track. Our efforts were acknowledged in the UCSC Genome Browser News May 4th data release for SARS-CoV-2 genome browser (May 4, 2020) and the associated manuscript. It’s a small but potentially impactful contribution by non-clinicians in the UCSF School of Nursing's research community to support the herculean efforts of our clinician colleagues.
  • ShinyGAStool is an open source software program developed with Thomas Hoffman that enables the user to perform a candidate gene association analysis from large datasets in an easy to use interface. With a four-step workflow, shinyGAStool successfully allows the user to access genome-wide datasets, incorporate metadata (e.g., phenotypic data), select genes and SNPs to evaluate, and identify co-variates, and perform the regression analysis. ShinyGAStool is implemented as a shiny application in the R programming language. The software and usage instructions are publicly available online at: https://github.com/kordk/shinyGAStool
  • Multiple Alignment of Reference and Short readS (MARSS) is a software tool developed with Samantha Danison and Grant Pogson to generate Multiple Species Consensus Alignments and quality control statistics for comparative genomics analyses of regions of a reference genome from aligned reads. It also implements a test to identify and score potential paralogs in whole genome or transcriptome sequencing comparative genomics studies. MARSS was developed with high throughput in mind and can be implemented in high performance computing environment for comparative genome analyses using next-generation short read technology. https://github.com/kordk/marss
  • KP Singh, C Miaskowski, A Dhruva, E Flowers, KM Kober. Mechanisms and Measurement of Changes in Gene Expression. Biological Research for Nursing. 2018. PMC6346310
  • C Harris, C Miaskowski, A Dhruva, J Cataldo, K Kober. Multi-Staged Data-Integrated Multi-Omics Analysis for Symptom Science Research. Biological Research for Nursing. 2021. PMID 33827270.

Photo Credit: This cluster graph from UCSF Profiles shows the co-authors (green circles) and top co-authors of co-authors (blue circles) of Kord Kober (red circle). The size of a circle is proportional to the number of publications that author has. The thickness of a line connecting two authors' names is proportional to the number of publications that they share. Co-authors included are limited to only those having a Profile (e.g., UCSF and UC Berkeley).