Humans are an intrepid race. For centuries, explorers have embarked on ambitious journeys of discovery toward distant horizons in search of new lands and unfriendly shores. They were pioneers with limited resources, and powerful ideals. Today a new generation of visionary scientists sail to new frontiers and discovery. They sail with knowledge, strategy, timing and multiple resources to reach a destination and achieve a goal, seeking to develop new cure for human diseases, consciously engaging in development and innovation. Come aboard with me for this crusade to fight human cancer.
Cancer is a collection of related diseases in which some of the body’s cells begin to divide without stopping and spread into surrounding tissues. There are at least 200 forms of cancer, and many more subtypes. Lung, prostate, and breast cancer are a few of the most common cancers worldwide.
Different cancers can require different treatments, like chemotherapy, radiation therapy, or immunotherapy. But how cancers differ in types? How is the same cancer different among individuals?
A Data-Driven Journey in Cancer Research
Identifying the changes in each cancer’s complete set of DNAs – its genome– and understanding how such changes interact to drive the disease is the foundation for improving cancer prevention, early detection and treatment.
But how challenging! Finding the needle in such haystack of human proteins and genes demands a new kind of science.
In the last decades genetic approaches to cancer research have dramatically advanced our understanding of the pathophysiology of this disease. As a result, a torrent of information is now pouring out of digital protein expression databases and genomics projects.
Today I want to tell you why and how we, at the Human Protein Atlas, partnered with the Cancer Genome Atlas to create the Pathology Atlas.
Meet the Sailors
To untangle the biology of a disease such as cancer a pure list of data alone is not enough. We need to find patterns to know where and what to look for. We need experts and brave sailors. So, let me introduce them to you.
We begin with us, the Human Protein Atlas (HPA). We are a Swedish-based program initiated in 2003. In this journey we bring the map of all the human proteins in cells, tissues and organs. The proteomic data is the result of the integration of various omics technologies, including antibody-based imaging, mass spectrometry-based proteomics, transcriptomics and systems biology.
The other sailor is the Cancer Genome Atlas (TCGA): a collaboration between the National Cancer Institute and the National Human Genome Research Institute that has generated comprehensive, multi-dimensional maps of the key genomic changes in 33 types of cancer. The TCGA dataset, brings more than two petabytes of genomic data, and has been made publicly available, so that this genomic information helps the cancer research community to improve the prevention, diagnosis, and treatment of cancer.
All the data in the HPA and TGCA resources is open access to allow scientists both in academia and industry to freely access the data for exploration of the human proteome.
Why a Pathology Atlas?
Pathology is the study of disease. It is the bridge between science and medicine: it underpins every aspect of patient care, from diagnostic testing and treatment advice to using cutting-edge genetic technologies and preventing disease. On the front lines of diagnosing disease, pathologists are responsible for analyzing laboratory data and conducting research that adds critical detail to previous clinical studies to help bring us closer to identifying the sources of several diseases.
The Pathology Atlas is an open-access database and a critical part of the Human Protein Atlas with the scope of identifying the role of genes, RNA, proteins, and metabolites in the complex human cancerous tissues and organs.
Performing research using innovative electronic databases, such as the Pathology Atlas, allows pathologists to improve testing methods to predict and find new treatments to fight cancer.
How We Collaborate
Different bioinformatics and statistics tool were used to make sense from these data such as: 1) Kaplan-Meier survival plot of individual prognostic gene that relates to the length of survival in a patience. 2) Heat map showing the pairwise overlapping of prognostic genes between cancer types. 3) System integration of prognostic genes with human metabolic model. 4) Network plot of co-expression genes in a certain cancer type.
Collaboration Turns Ideas into Innovations
Our collaborative research journey reports several important findings related to cancer biology and treatment. In our journey, we have identified candidate prognostic genes associated with clinical outcome for each tumor type and generated metabolic models for individual patients. A summary of the results is listed below.
Briefly we found that:
- Many genes are differentially expressed in cancers, and a large proportion of these genes have an impact on overall patient survival.
- Shorter patient survival is generally associated with up-regulation of genes involved in mitosis and cell growth, and down-regulation of genes involved in cellular differentiation.
- The gene expression patterns of individual tumors varied considerably and could exceed the variation observed between different cancer types.
- A gene may be prognostic in more than one cancer type.
Read our full paper about the Pathology Atlas published in Science here.
Figure 1. The Pathology Atlas includes 17 cancer types. Here you can explore mRNA and protein expression data, and read about genes associated with prognosis. Image from the Human Protein Atlas.
Sailing Over 17 Cancer Types Searching for the Missing Key
The cancer proteomes presented in the Pathology Atlas, are interactive chapters with mRNA, protein expression data and genes associated with prognosis from 17 different forms of human cancer. The cancer proteome pages are organized according to normal tissue of origin and each page can be accessed by clicking on a cancer type name or on a symbol in the figure.
Gene Signature: Prognostic Gene Discovery
A gene signature is a single or combined group of genes in a cell or tissue with a uniquely characteristic pattern of expression that occurs as a result of an altered biological process or pathogenic medical condition, such as cancer.
Prognosis refers to foreseeing the probable outcome or course of a disease. A prognostic gene is not a target of therapy, but it offers additional information to consider when discussing details such as duration or dosage or drug sensitivity in the therapeutic intervention phase.
The Pathology Atlas focuses on identifying prognostic gene signatures with the hopes of improving the diagnostic methods and therapeutic courses adopted in a clinical setting. Correlation analysis of gene expression data and patient survival data collected in the Pathology Atlas, resulted in more than 10,000 prognostic genes.
Figure 2. The Loot in this Treasure Hunt: the Pathology Atlas is treasure chest of data.
Evolution of Precision Medicine
Decades of research have dramatically increased our knowledge about cancer diagnosis, treatment and management, resulting in better and safer outcomes for many patients. Currently available drugs can fight cancer in three major ways:
- Inhibiting enzymes that trigger the abnormal growth and survival of cancer cells
- Blocking aberrant gene expression characteristics of cancer cells
- Halting molecular signaling pathways that are in overdrive in cancer cells
However, due to the interindividual tumor heterogeneity, most cancer drugs are effective only in a subgroup of patients. But this is where the Pathology Atlas can make a difference.
Figure 3. In this interactive table you can find the number of prognostic genes with regard to cancer type and association with favorable or unfavorable clinical outcome. All data is supported by lots of examples and hundreds of stained images. Image from the Human Protein Atlas.
Buried Beneath the Data Lays Your Personal Key
Fellow sailors our journey has been fruitful. Altogether our data allows for generation of personalized genome-scale metabolic models for cancer patients to identify key genes involved in tumor growth.
Today the Pathology Atlas helps to discover unique therapies that treat an individual’s cancer based on the specific abnormalities of their tumor. In fact, once a candidate protein has been identified, scientists can gain a better understanding of the molecular basis of cancer growth, metastasis, drug resistance and, before long, target cancer therapies (based on either small molecules or monoclonal antibodies) can be developed.
Stay tuned and follow my journey!
I feel like one of those traveler diarists whose unique insights, perspectives and limitless inspiration have guided humanity through amazing discovery journeys. Sailors say that every expedition is exciting, it is a thrill that never leaves you.
But every destination leads to new challenges and many possible answers, none of which are complete. So, here I am continuing to chase each possibility and explore more.
Are you ready to step onboard? The treasure hunt has just started. Start exploring the Pathology Atlas here.
|Interested in learning more about the Human Protein Atlas? Read our previous blog post presenting another part of the atlas, the Tissue Atlas.|