MBioLIMS Projects

The following interview was conducted with the leader of Kober lab, Dr. Kord Kober.

1. What are the primary focus(es) of your research?

My program of research focuses on a few overlapping themes. First, we use a systems biology approach that centers around using phenotypic and multi-omic data to improve our understanding of the molecular mechanisms underlying common symptoms (e.g., fatigue, neuropathy) experienced by oncology patients. These adverse sequelae affect patients’ daily activities and quality of life, as well as have a negative impact on their ability to tolerate treatment. The identification of targets for treatment requires an understanding of the underlying molecular mechanisms. With these targets, we can identify new drugs or non-pharmacological interventions that can be evaluated in preclinical and clinical studies. Second, we use data science and machine learning approaches to develop models to predict the severity of these outcomes across the course of a patient’s treatment and into survivorship. These prediction models can be an important component of effective symptom management for patients.  Finally, 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 goal that others may benefit from their usage in their own research projects. We are funded by the American Cancer Society and the National Cancer Institute, most recently with an NCI R37 MERIT award.

2. Who makes up your team and what role do they play in conducting strong research?

My research team is comprised of a mix of staff and student trainees. First, our lab manager Esther Chavez-Iglesias, provides the management and administration of the lab, including the MBioLIMS deployment, and she performs the experiments associated with our program of research. These experiments include molecular cell biology, specimen processing, and specimen banking. Supporting Esther is Anatol Sucher, who served as the previous lab manager for almost two decades and is now retired. Anatol developed the previous in-house LIMS system and has supported our migration to MBioLIMS. Finally, we have an active group of trainees. Caroline Le is a post-bac trainee whose work in the lab focused on multi-omics analysis associated with fatigue and lymphedema. Nidhi Thati is an undergraduate trainee whose work in the lab first focused on the development of DNA isolation methods from stool samples and is now engaged in the evaluation of molecular mechanisms of fatigue. Previous trainees include nurses, residents, undergraduates, youths (i.e., high school), and physicians.

3. What types of specimens do you collect and how do you process and analyze them? 

We collect a variety of specimens.  For our work and those of our collaborators, we collect clinical samples across numerous tissue types (e.g., peripheral blood, stool, vaginal swabs, saliva, placenta, cord blood, urine, breast milk, nasal swabs, heart). In addition to our own research studies, our lab has provided specimen banking and processing for over 20 studies across the University (e.g., Pre-Term Birth Initiative, HOPE COVID-19, Sexual Minority Stress, Women’s Interagency HIV Study, Symptom Cluster Study, Lymphedema Study, ARTEMIS), including specimens from over 7,000 study participants over the past two decades. Some common processing activities related to biobanking activities are nucleic acid isolation, quality control, and specimen preparation for high-throughput assays. Although we are proud of the success of biobanking and biospecimen processing collaborations we have provided, this work is now out of our scope and we encourage researchers to use The Biospecimen Processing Lab (BSPL) or other dedicated services available on campus.

4. How do you measure the success of your research?

We measure success through mentee development and active involvement in the projects, our stewardship of samples from our and others' studies, and continued communication of findings. We strive to continue to make progress in the identification of targets for treatment and the development of compounds or other interventions to treat these targets with the goal of supporting the improvement of patient care.

5. Can you share how the research you've done has/could contribute to the broader scientific community?

Our work in transcriptomics and epigenomics has identified perturbations in inflammatory, neuroendocrine, neurological, and other pathways associated with fatigue and neuropathy. These analyses identified potential targets for therapeutic interventions for these common and devastating clinical problems. These works also provided the foundation for numerous other studies by our mentees identifying genes and pathways associated with shortness of breath, cognitive changes, anxiety, sleep, nausea, and clusters of symptoms.

We are using machine learning approaches to accurately predict the severity of fatigue from prior to through the week following the administration of chemotherapy using demographic, clinical, psychosocial, and molecular characteristics.  These studies are the first to use machine learning techniques to predict evening and morning fatigue severity in the week following chemotherapy from fatigue scores obtained in the week prior to chemotherapy. Our findings suggest that the language used to assess clinical fatigue in oncology patients is important and that two simple questions may be used to predict morning and evening fatigue severity. In addition, the prediction performance of our models did not demonstrate bias in terms of self-reported race and ethnicity, self-reported sex, or self-reported income.

Finally, to enable broad accessibility to the research community to perform expression quantitative trait methylation (eQTM) mapping to identify expression-associated CpG (eCpG) loci, we developed Torch-eCpG (https://github.com/kordk/torch-ecpg). This software tool is a GPU enabled python CLI using pytorch and is free to use (i.e., open source). This tool is the first publicly available open-source eQTM mapping tool.

6. What is your favorite part of the work you do?

There are numerous aspects of the work we do that I enjoy, including the collaboration and mentoring, the excitement of new findings and contributions to science, and the knowledge that our work will ultimately contribute to helping patients.

7. Are there things about MBioLIMS that have been helpful to your operation?

We are a smaller lab operation with the goal of adapting our previous specimen tracking practices to one that is congruent with the broader campus offerings. The system offers a number of advantages to our in-house development, including centralized deployment, common training for staff members, consistent practice across staff turnover, scalability for future projects, and affordability. Our experience with the ARS team in the development and deployment of our study biospecimen database with the MBioLIMS environment has been excellent. David, Nicola, Emily, Julian, and the team have been helpful, responsive, and supportive. 

The MBioLIMS team has been very supportive in our deployment of the system.

8. Are there any links to your work that you'd like us to share? 

Our Lab Website is https://kober.ucsf.edu

The following interview was conducted with Ronald Honrada, Associate Director of CTSI’s Biospecimen Processing Lab (BSPL) 

Ron runs the Biospecimen Processing Lab, which provides a suite of biospecimen services to support clinical, translational, and basic life sciences research.  Services BSPL provides include:  Tissue Acquisition, Biofluid Processing, and Biospecimen Custodianship & Storage. 

 Can you describe how operations have been streamlined in your labs? 

We’ve made significant strides towards streamlining operations across our multiple BSPL lab sites.  One of the major changes has been the centralization of data, which allows us to replace previous systems like Excel and RedCap.   Now, we log samples in real time using auto-generated child sample records and a unique barcode for each aliquot.  This speeds up sample processing for our lab technicians.   The field validation in MBioLIMS significantly reduces data entry errors resulting in more accurate data around the biospecimen lifecycle for BSPL-managed samples.  Study teams can also directly access their sample collection data in MBioLIMS.  By enabling the study team to proactively access and monitor their own data, use of MBioLIMS streamlines our operational workflows and optimizes the use of limited resources which is critical given current staffing levels and budget constraints. 

How has the cost recovery process been improved? 

The cost recovery process has been greatly improved since our collaboration with the BSIP team. We’ve implemented a system where each sample is tagged with its service cost at time of collection, which streamlines the billing process. With MBioLIMS, we can export monthly billing data into iLab with just one click, providing a clear audit trail from MBioLIMS to iLab. This results in faster invoicing, more accurate billing, and provides a greater level of transparency to BSPLs customers and business partners.  

What does your program do to ensure compliance and be prepared for audits? 

Ensuring compliance and audit readiness is a top priority for us.  MBioLIMS is PHI-secure and adheres to UCSF’s stringent security standards. MBioLIMS has permissions specific to each user type and study and it maintains a full audit trail, both of which support good lab practices and simplify audit responses.  The system allows easy access to historical data, including chain of custody records, processing details, storage history, and shipping logs. This allows us to be well prepared for audits and to be able to provide comprehensive and accurate documentation as required. 

How has MBioLIMS impacted your overall lab activities? 

MBioLIMS has had a very positive impact on our lab activities. The software assists in streamlining operations, improving billing processes, and enhancing compliance, and it has allowed us to focus more on supporting study teams and advancing research. The improved efficiencies in data entry saves time and resources, which are particularly valuable during challenging periods like hiring freezes and budget constraints. Ultimately, MBioLIMS enables us to better serve our research community and contribute more effectively to scientific advancements.