Welcome to The Visible Embryo
The Visible Embryo Home
Home--- -History-----Bibliography-----Pregnancy Timeline-----Prescription Drugs in Pregnancy---- Pregnancy Calculator----Female Reproductive System----News----Contact

WHO International Clinical Trials Registry Platform

The World Health Organization (WHO) has a Web site to help researchers, doctors and patients obtain information on clinical trials.

Now you can search all such registers to identify clinical trial research around the world!




Pregnancy Timeline

Prescription Drug Effects on Pregnancy

Pregnancy Calculator

Female Reproductive System


Disclaimer: The Visible Embryo web site is provided for your general information only. The information contained on this site should not be treated as a substitute for medical, legal or other professional advice. Neither is The Visible Embryo responsible or liable for the contents of any websites of third parties which are listed on this site.

Content protected under a Creative Commons License.
No dirivative works may be made or used for commercial purposes.


Pregnancy Timeline by SemestersDevelopmental TimelineFertilizationFirst TrimesterSecond TrimesterThird TrimesterFirst Thin Layer of Skin AppearsEnd of Embryonic PeriodEnd of Embryonic PeriodFemale Reproductive SystemBeginning Cerebral HemispheresA Four Chambered HeartFirst Detectable Brain WavesThe Appearance of SomitesBasic Brain Structure in PlaceHeartbeat can be detectedHeartbeat can be detectedFinger and toe prints appearFinger and toe prints appearFetal sexual organs visibleBrown fat surrounds lymphatic systemBone marrow starts making blood cellsBone marrow starts making blood cellsInner Ear Bones HardenSensory brain waves begin to activateSensory brain waves begin to activateFetal liver is producing blood cellsBrain convolutions beginBrain convolutions beginImmune system beginningWhite fat begins to be madeHead may position into pelvisWhite fat begins to be madePeriod of rapid brain growthFull TermHead may position into pelvisImmune system beginningLungs begin to produce surfactant
CLICK ON weeks 0 - 40 and follow along every 2 weeks of fetal development


How to calculate an immune response

Using patient gene data, Dartmouth team quickly calculates personal immune response profiles for thousands of patients.

Immunotherapy is the harnessing of a patient's immune system to help fight cancer, and it is showing great potential as a treatment. A newly published study from a team at Dartmouth-Hitchcock's Norris Cotton Cancer Center (NCCC) helps illustrate the complex interactions between immune cell types in tumor microenvironments.

Researchers have now created a new mathematical model to interpret how immune cells might be infiltrating cancers. They based it on each patient's own gene data, their specific type of cancer, as well as their particular immune response to that cancer. This algorithm enabled them to quickly calculate a personal immune response profile for thousands of patients. Their findings, Systematic Pan-Cancer Analysis Reveals Immune Cell Interactions in the Tumor Microenvironment, are published in Cancer Research.

The work took place in the laboratory of Chao Cheng PhD, at NCCC, in Dartmouth. It was led by Fred Varn BS, a graduate student and PhD candidate in Cheng's laboratory.

"We were curious about how different immune cells affect each other in different cancer types. Some immune cells, such as CD8+T cells, have tumor-killing capabilities while others, such as T-regulatory cells and certain myeloid cells, suppress these attacks.

"Understanding how these cell types infiltrate different tumors, and the effect these cells have on each other and the patient, can help us understand how to better harness the power of the immune system for cancer therapy."

Frederick S. Varn PhD, Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth College, Hanover, New Hampshire, USA.

The team computed a method to outline patterns in immune infiltration by unique tumor cell types. They found most immune cells tend to infiltrate a tumor all at once. This demonstrates the importance of having a full patient immune response profile to determine how a single immune response will affect a patient.

They validated their concept by showing that patient survival benefits from "good" (tumor-killing) immune infiltrations, that were later modified through infiltrations by immunosuppressive cell types.

The study also examined what causes patients to have different immune infiltration patterns — finding tumors with a higher than average mutation count tend to have higher immune infiltration.

However, when looking at a single cancer, mutation count did not associate with increase in mutation burden. This suggests something else may drive differences in immune infiltration between patients with the same type of tumor.

"Our study uses a computational method that can be cheaply and easily applied to patient gene expression profiles to explore patients' baseline tumor immune response" explains Varn. "This information can be used to help identify patients likely to respond to certain immunotherapeutic approaches."

Looking ahead, they hope to be able to predict which patients are likely to respond to immunotherapy. To accomplish this, they will need individual datasets which include both (1) gene expression information to infer a patient's immune infiltration profile, and (2) patient immunotherapy response information.

Currently, very few of these datasets have been made publicly available. However, as immunotherapeutic approaches continue to develop, Varn and his team anticipate more of these datasets will be constructed. This will allow the team to use their method for predicting immunotherapy responders.

"Our study is the first, to our knowledge, that uses computational approaches to examine the effect different immune cells have on each other — in the context of the tumor — and outline how these interactions affect survival,"

Frederick S. Varn PhD

A patient's personal immune response profile can greatly affect their prognosis and likely affects their response to immunotherapeutic approaches. It is not enough to simply measure the level of one cell type, as another cell type may also impact immune cell behavior. How to increase tumor-killing cell activity and decrease immunosuppressive cell activity is the important question going forward.

With the recent advent of immunotherapy, there is a critical need to understand immune cell interactions in the tumor microenvironment in both pan-cancer and tissue-specific contexts. Multi-dimensional datasets have enabled systematic approaches to dissect these interactions in large numbers of patients, furthering our understanding of the patient immune response to solid tumors. Using an integrated approach, we infered the infiltration levels of distinct immune cell subsets in 23 tumor types from The Cancer Genome Atlas. From these quantities, we constructed a co-infiltration network, revealing interactions between cytolytic cells and myeloid cells in the tumor microenvironment. By integrating patient mutation data, we show that while mutation burden was associated with immune infiltration differences between distinct tumor types, additional factors may explain immunogenic differences between tumors originating from the same tissue. Finally, we examined the prognostic value of individual immune cell subsets as well as how co-infiltration of functionally discordant cell types associated with patient survival. We showed in multiple tumor types that the protective effect of CD8+ T cell infiltration was heavily modulated by co-infiltration of macrophages and other myeloid cell types, suggesting the involvement of myeloid-derived suppressor cells in tumor development. Our findings illustrate complex interactions between different immune cell types in the tumor microenvironment and indicate these interactions play meaningful roles in patient survival. These results demonstrate the importance of personalized immune response profiles when studying the factors underlying tumor immunogenicity and immunotherapy response.

Received September 12, 2016.
Revision received December 16, 2016.
Accepted December 22, 2016.

This study was supported by grants from American Cancer Society (IRG-82-003-30), National Center for Advancing Translational Sciences at the National Institutes of Health (UL1TR001086) and National Institute of General Medical Sciences of the National Institutes of Health (T32GM008704).

About Norris Cotton Cancer Center at Dartmouth-Hitchcock
Norris Cotton Cancer Center combines advanced cancer research at Dartmouth's Geisel School of Medicine with patient-centered cancer care provided at Dartmouth-Hitchcock Medical Center in Lebanon, NH, at Dartmouth-Hitchcock regional locations in Manchester, Nashua and Keene, NH, and St. Johnsbury, VT, and at partner hospitals throughout New Hampshire and Vermont. It is one of 45 centers nationwide to earn the National Cancer Institute's "Comprehensive Cancer Center" designation. Learn more about Norris Cotton Cancer Center research, programs, and clinical trials online at cancer.dartmouth.edu.
Return to top of page

Mar 31, 2017   Fetal Timeline   Maternal Timeline   News   News Archive   

Dartmouth research uses a new computational method to define immune cell interactions within
tumor microenvironments. The method quickly calculates individual immune response profiles.
Image Credit: Frederick S. Varn


Phospholid by Wikipedia