Hong Zheng

Lecture Notes

Hong Zheng / 2017-12-04

10X genomics workshop

How do we learn what works? a two-step algorithm for causal inference from real world data

Miguel Hernan, DrPH, Professor of Biostatistics and of Epidemiology

Decisions must be made now, in clinical practice and publich health.
How do we estimate causal effects? Randomized trials.
If randomized trials are not feasible, then we use observational data.

The target trial concept leads to a simple two-step algorithm for causal inference

Example: postmenopausal hormone theray and coronary heart disease

AACR 2018

Why do lung adenocarcinomas respond to kinase inhibitors while glioblastomas don’t? Contrasting patterns of tumor evolution

Matthew L. Meyerson. Dana-Farber Cancer Inst., Boston, MA

The making of the pre-cancer atlas: Opportunities and challenges

Sudhir Srivastava. National Cancer Inst., Rockville, MD

Examples of precancerous lesions

Single-cell genomics for the analysis of premalignancy

Nicholas E. Navin. UT MD Anderson Cancer Ctr., Bellaire, TX

Genomic analysis of precancers in the GI tract

Eduardo Vilar-Sanchez. UT MD Anderson Cancer Ctr., Houston, TX

Cancer evolution measured at single cell resolution

Sohrab Shah. BC Cancer Agency Vancouver Ctr., Vancouver, BC, Canada

Quantifying patient-specific evolutionary dynamics

Christina Curtis. Stanford University, Stanford, CA

Highly Sensitive Tumor Detection and Classification using Methylome Analysis of Plasma cfDNA

Daniel Diniz De Carvalho. Princess Margaret Cancer Centre, Toronto, ON, Canada

Non-invasive detection of cancers in plasma with DNA methylation haplotypes

Kun Zhang. University of California, San Diego, La Jolla, CA

Detecting early-stage CRC with Guoxiang Cai, Fudan
specificy sensentivity
healthy 93.3 -
adenoma - 75
CRC plasma, stage 1 - 73.4
CRC plasma, stage 2 - 78.7
CRC plasma, stage 3 - 88
CRC plasma, stage 4 - 100

Cancer locator: Harnessing the diagnostic potential of cell-free DNA methylation

Jasmine Xianghong Zhou. University of California at Los Angeles, Los Angeles, CA

Epigenetic traces in plasma DNA

Michael R. Speicher. Medical University of Graz, Graz, Austria

Sequence based DNA methylation analysis

Martin Hirst. The University of British Columbia, Vancouver, BC, Canada (http://hirstlab.msl.ubc.ca/)

How and why look for clusters of cis-regulatory elements (COREs, aka super-enhancers) in cancer

Mathieu Lupien. Princess Margaret Cancer Ctr., Toronto, ON, Canada

CREAM: Clustering of genomic REgions Analysis Method (https://www.biorxiv.org/content/early/2018/03/20/222562)

N-of-1 networks to personalized cancer treatment

Josh Stuart, 11/18/2017

Cancers come in several forms according to the organ and tissue of origin, the type of mutagen and the impacted genetic pathways that contribute to oncogenic progression. Pan-Cancer analyses across multiple types of cancers, using multiple types of omics data, have identified molecular-based subtypes of clinical importance. Even so, patients may not respond to the usual treatment regime and carry their own unique alterations. I will discuss network integration strategies for building patient-specific networks to model the aberrant wiring in a single person’s tumor. The goal is to then strategies treatment for the person based on critical nodes in the uncovered network.

Identify closest cancer form
Identify impactful variants
Identify an explanatory network model

Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

tissue-of-origin signals, 10-20% reclassified associated w/ survial

Personal regulome navigation

Howard Chang, 11/2/2017

Regulome but not genome mutation pattern predicts clinical response to HDACi


T-ATAC: Transcript-indexed ATAC pairs single cell RNA and ATAC-Seq


Precision leukemia diagnosis with T-ATAC

Enhancer connectome in primary T cells


Discoveries and opportunities for translation using Vanderbilt’s Gene X Medical Phenome Catalog

Nancy Cox, 11/2/2017


Epigenomics signatures

Joseph Ecker

Organisms with identical genomes can exhibits distinct phenotypes, such as plants (fwa-1 vs. wt), insects, mammals.

DNA methylation dynamics


From genomic variation to molecular mechanism

Jan Korbel

Germline determinants of the somatic mutation landscape in 2,642 cancer genomes https://www.biorxiv.org/content/biorxiv/early/2017/11/01/208330.full.pdf

Structure variant discovery by paired-end sequencing

SVs associated with repetitive DNA: model for inversion information in the human genome. They are extremely difficult to detect, and are overlooked using main-stream NGS.


Big data in biology

Ewan Birney

Human: the new model organism


Medaka fish

Advances in Cancer Immunotheray

Edgar Engleman, 11/1/2017

Bariers to effective immunotherapy

Early approach: tumor-binding mAbs

T cells can recognize and kill tumor cells

Another early approach: dentritic cells to stimulate T cell mediated anti-tumor immunity

Chimeric antigen receptor (CAR) T cells

Immue checkpoint blocker

Circulating tumor DNA analysis for cancer detection and monitoring

Max Diehn, 10/19/2017


Detection methods (Detection limit)

Development of CAPP-seq

Challenges: limited input molecules,low fractional abundance, inter-patient heterogeneity


Molecular barcoding for error repression

Stereotyped errors in cfDNA NGS data

Decreasing sequencing errors in cfDNA sequencing, iDES-enhanced CAPP-Seq

10cc blood 5cc plasma, 30ng cfDNA, 5000 hGEs Analytic sensitivy : generalized 0.002%, personalized 0.00025%

Minimal residual disease (MRD)

Small volumns of tumor cells remaining after treatment in patients who have no clinical evidence of disease

ctDNA MRD detection has been demonstrated in breast and colon cancers. How about lung cancer?

ctDNA is prognostic in node-negative patients. Patients with non-detection MRD cfDNA has longer survial.

ctDNA detections precedes clinical relapse.

Prospect for personalization of postradiotheray adjuvant treatment