There was a time when cancers were almost entirely defined by their tissue of origin. Patients were diagnosed with a cancer of a particular organ and treated--with varying degrees of success--according to these classifications. However, research over the past 50 years has revolutionized our understanding of malignant progression at the genomic level and established the view that cancers could also be grouped by the genetic alterations that drove their development. While this work has uncovered a bewildering complexity of genetic and epigenetic alterations in tumors, it also showed that mutations in the same oncogenes and tumor suppressor genes are found time and again in multiple types of cancer. These driver mutations play a fundamental causative role in the development of almost all cancers, suggesting that under the hood, even cancers of diverse origins have key similarities.
We are in the midst of a renaissance in cancer genetics. Over the past several decades, candidate-based targeted sequencing efforts provided a steady stream of information on the genetic drivers for certain cancer types. However, with recent technological advances in DNA sequencing, this stream has become a torrent of unbiased genetic information revealing the frequencies and patterns of point mutations and copy number variations (CNVs) across the entire spectrum of cancers. One of the most important observations from this work is that genetic alterations in bona fide cancer drivers (those genes that, when mutated, promote tumorigenesis) show a remarkable spectrum of tissue specificity: Alterations in certain driver genes appear only in cancers derived from one or a few tissue types (1). Here, we discuss the concept of tissue specificity of genetic alterations in cancer and provide general hypotheses to help explain this biological phenomenon.
Approximately 95 percent of cancer mortality is caused by metastasis. This fact is what motivates many cancer researchers to focus on finding new ways to stop or kill the growth of metastatic cancer cells. A new paper published in Nature Genetics focuses on research done to find out how cancers metastasize using new next-generation sequencing tools, specifically targeting endometrial cancer. The researchers performed whole-exome sequencing of 98 tumor biopsies and analyzed The Cancer Genome Atlas (TCGA) data to identify new recurrent alterations in primary tumors. William Gibson, a first author of the paper and a current MD-PhD student in the Harvard-MIT Health Sciences and Technology program, explains that it is often difficult to obtain paired samples of primary and metastatic tumors: "One cohort of patients in whom you can obtain primary and metastatic tissue is in endometrial cancer patients for whom both the primary tumor and abdominal metastases are resected during the same operation.
In 2017, we've reported on everything from a cancer "kill switch" and AI-assisted cancer detection, to the first tests of a cancer vaccine and the creation of a portable skin cancer detector. These stories were just the tip of the iceberg in terms of exciting developments in cancer research this year.
A new computational approach that allows the identification of molecular alterations associated with prognosis and resistance to therapy of different types of cancer was developed by the research group led by Nuno Barbosa Morais, Group Leader at Instituto de Medicina Molecular João Lobo Antunes (iMM; Portugal), and now published open access in Nucleic Acids Research*. Cancer cells are characterised by perturbations in the regulation of genes and, in particular, by alterations in alternative splicing, a process by which the same gene can originate different proteins. Some of those alterations are associated with different malignant features of cancer and its resistance to treatment but vary from tumour to tumour. "Each patient hosts a different cancer, so that scientists and clinicians need molecular information about many individuals to, supported by data, understand disease mechanisms, assess prognosis and make predictions on the best treatment for each patient based on their tumour's molecular profile", explains Nuno Barbosa Morais. "We have created a software that, by analysing large databases with clinical and splicing information for thousands of tumours, detects patterns of similarities between different cases and allows, for instance, to identify the relation of each molecular alteration with patient survival, for more than thirty types of cancer.