Posts classified under: Fall 2022

BIODS 260
Dr. Ajit Johnson Nirmal
9/29/22
1:30 pm-2:30 pm
Dr. Ajit Johnson Nirmal, Instructor, Dana-Farber Cancer Institute

Seminar Title: The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution

Abstract: Cutaneous melanoma is a highly immunogenic malignancy that is surgically curable at early stages but life-threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially resolved microregion transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis involving precursor states, melanoma in situ, and invasive tumor. Hallmarks of immunosuppression are already detectable in precursor regions. When tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor–stromal boundary. This environment is established by cytokine gradients that promote expression of MHC-II and IDO1, and by PD1–PDL1-mediated cell contacts involving macrophages, dendritic cells, and T cells. A few millimeters away, cytotoxic T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can coexist within a few millimeters of each other in a single specimen.

Suggested reading: 

The Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution

Zoom link: HTTPS://STANFORD.ZOOM.US/J/95499501659? PWD=TLFQTLBRM1JSEGXXWXFFELFIQLBCZZ09&FROM=ADDON PASSWORD: 406712

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BIODS 260
Roxana Daneshjou
10/06/22
1:30 pm-2:30 pm
Dr. Roxana Daneshjou

Location: MSOB x303 

Seminar Title: Precision health for all: developing inclusive datasets and algorithms

Abstract: Large biomedical datasets coupled with machine learning tools have the potential to transform the practice of dermatology. For example, analysis of skin disease images could help triage patients prior to the clinical visit and precision genomic medicine could identify personalized treatments for skin disease. However, biased datasets and algorithms that exclude underrepresented groups could exacerbate existing health disparities in dermatology. This talk will discuss working towards inclusive precision medicine through three examples: 1) assessing fairness in datasets and AI algorithms used for diagnosing disease in dermatology 2) developing an inclusive patient-facing algorithm to improve the quality of images submitted for teledermatology and 3) developing a pharmacogenomics algorithm that accounts for population diversity.  In order to develop a data-driven approach to dermatology that improves health disparities, rather than exacerbating them, we must be mindful of developing inclusive datasets and algorithms.

Suggested Readings:

• https://www.science.org/doi/10.1126/sciadv.abq6147
• https://jamanetwork.com/journals/jamadermatology/article-abstract/2784295
• https://ashpublications.org/blood/article/124/14/2298/33101/Genetic-variant-in-folate-homeostasis-is

BIODS 260
10/13/22
1:30 pm-2:30 pm
Lynn Petukhova
Assistant Professor, Epidemiology and Dermatology at the Columbia University Medical Center

Title: Leveraging Information in the Human Genome to Improve Skin Health and to Advance the Practice of Dermatology

Abstract: The process of diagnosing a patient historically has largely relied on clinical observations of symptoms by physicians. Limitations of a clinical diagnosis have been identified with the use of genetic and genomic technologies, which demonstrate that a molecular diagnosis derived from biomedical data can provide greater diagnostic accuracy and inform subsequent management. I conduct human genetic studies as a starting point for leveraging information in the human genome to improve the accuracy and utility of a skin disease diagnosis. Statistical evidence for an association between an inherited genetic variant and a disease outcome is a definitive marker for a disease mechanism, but does not provide adequate resolution of the mechanism for clinical translation. The scale and complexity of biomedical data that is available to define disease mechanisms requires data-driven approaches to identify salient features and to detect patterns among them that link disease mechanisms to interventions and outcomes. Using the hair follicle as a model organ to understand mappings between disease mechanisms and clinical diagnoses, our group is using clustering, network, and tensor factorization methods to discover clinically relevant relationships among genetically-derived disease entities. I will present results from three studies that our group is conducting that leverages knowledge about inherited genetic variants, disease genes, pathways, and/or comorbidities to define an underlying causal structure of skin disease pathogenesis and to identify key genetic regulators of hair follicle health.

Suggested Readings:

https://www.nature.com/articles/s41598-017-16050-9

https://dl.acm.org/doi/abs/10.1145/3368555.3384464

Zoom link: https://stanford.zoom.us/j/92865685887pwd=YjlUM1cxOHZ4UnBZMkhqcG1JYzFNdz09

Meeting ID: 928 6568 5887

Password: 219826

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BIODS 260 presents:
Dr. Lee Hood
10/20/22
1:30 pm-2:50 pm

Dr. Leroy Hood, MD, PhD, CEO/Founder of Phenome Health and Chief Strategy Officer and Professor of the Institute of Systems Biology

Suggested readings:

• A wellness study of 108 individuals using personal, dense, dynamic data clouds; 2017; ND Price et al; Nature Biotechnology
• Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19; 2020; Y Su et al; Cell
• Multiomic blood correlates of genetic risk identify presymptomatic disease alterations; 2020; M Wainberg et al; Proceedings of the National Academy of Sciences 
• Untargeted longitudinal analysis of a wellness cohort identifies markers of metastatic cancer years prior to diagnosis; 2020; AT Magis et al; Scientific Reports

Zoom Link:

https://stanford.zoom.us/j/92874055477pwd=aThzNmpmNEQ1L2FjV0E5ZXF5SDR1UT09&from=addon

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