The first step towards a "Cancer Dependency Map"

That most cancers use the same set of molecular tools is a very powerful idea, but it has been hard to figure out what these tools are and how to target them. Follow the link below for a quick, and enthusiastic, summary of genome-scale CRISPR–Cas9 screens of 324 human cancer cell lines from 30 cancer types with the goal of developing a new, diverse and more effective portfolio of cancer drug targets.

'Dismantling cancer' reveals weak spots

James Gallagher, BBC News

Scientists have taken cancer apart piece-by-piece to reveal its weaknesses, and come up with new ideas for treatment. A team at the Wellcome Sanger Institute disabled every genetic instruction, one at a time, inside 30 types of cancer. It has thrown up 600 new cancer vulnerabilities and each could be the target of a drug.Cancer Research UK praised the sheer scale of the study.

The study heralds the future of personalised cancer medicine. At the moment drugs like chemotherapy cause damage throughout the body. One of the researchers is Dr Fiona Behan, whose mother died after getting cancer for the second time.

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Another helpful summary of the recent paper linking jumping genes to cancer

A recent post discussed an important paper that demonstrated an association between transposable elements, aka ‘jumping genes’, and cancer. Transposable elements are an important but often forgotten class of mutagen that can contribute to genome instability and may disrupt genes and their expression. The original article has an outstanding abundance of data and is not easy reading. The article below provides an accessible summary that’s worth reading if you aren’t an expert in genomics.

Cancer: Scientists find 129 'jumping genes' that drive tumor growth

Catharine Paddock, Medical News Today

In cancer research, scientists usually look for cancer genes by scouring the genome for altered sequences — or mutations — in DNA. But a new study has now revealed that jumping genes, which customary sequencing overlooks, are also important drivers of tumor growth.

Scientists at the Washington University School of Medicine in St. Louis, MO, found that jumping genes are widespread in cancer and promote tumor growth by forcing cancer genes to remain switched on.

They analyzed 7,769 tumor samples from 15 different types of cancer and found 129 jumping genes that can drive tumor growth through their influence on 106 different cancer genes.

The jumping genes were functioning as "stealthy on-switches" in 3,864 of the tumors that the team analyzed. These tumors came from breast, colon, lung, skin, prostate, brain, and other types of cancer.

A recent Nature Genetics paper gives a full account of the study.

Gene-Expression Profiling to Understand Cancers of Unknown Origins

Phase 2 Trial Examines Gene-Expression Profiling for Cancer of Unknown Primary Site

A randomized phase 2 trial examining the assignment of treatment based on gene-expression profiling compared with standard chemotherapy for patients with cancer of unknown primary site showed no improvement in the 1-year survival rate with the more tailored approach. However, several caveats may limit the relevance of the findings. A report of this study was published in the Journal of Clinical Oncology.1

Cancer of unknown primary site (CUP) refers to malignancies in which the originating tumor type cannot be identified. As a result, determining the best treatment for this cancer, diagnosed in approximately 31,000 people in the US each year, is extremely difficult.2 In recent years, oncologists have looked to genetic testing to identify the cancer type as a way to improve care.

In the current study, a molecular analysis of biopsied tissue predicted the originating cancer site for all of the 101 patients treated. The analysis identified a total of 16 sites; cancers of the pancreas (21% of participants), gastric system (21% of participants), and malignant lymphomas (20% of patients) were the 3 most common sites to be predicted as the primary site of malignancy. The Japan-based researchers then randomized the patients to receive therapy appropriate to the predicted site of origin (50 patients) or the standard, empiric treatment of paclitaxel plus carboplatin (51 patients).

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‘Jumping genes’ drive many cancers

‘Jumping genes’ drive many cancers

Mistakes in DNA are known to drive cancer growth. But a new study, from Washington University School of Medicine in St. Louis, heavily implicates a genetic phenomenon commonly known as “jumping genes” in the growth of tumors.

The study is published March 29 in the journal Nature Genetics.

Since jumping genes aren’t mutations — mistakes in the letters of the DNA sequence — they can’t be identified by traditional cancer genome sequencing. As such, this study opens up new lines of research for future cancer therapies that might target such genes.

Jumping genes, which scientists call transposable elements, are short sections of the DNA sequence that have been incorporated randomly into the genome over the long course of human evolution. The evolutionary histories of jumping genes are the subject of much current research, but viral infection is thought to play an important role in their origins.

Researchers led by Ting Wang, PhD, the Sanford C. and Karen P. Loewentheil Distinguished Professor of Medicine, have plumbed genomic databases, looking specifically for tumors whose jumping genes are driving cancer growth.

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Specificity helps with cancer outcome prediction, therapies

Acute Erythroleukemia Genomic Subtypes Help Predict Outcomes, Suggest Therapies

NEW YORK (GenomeWeb) – A new genomic analysis of acute erythroid leukemia (AEL) has uncovered recurrent tumor gene mutation and expression profiles, including genomic features that appear to coincide with outcomes for patients affected by the rare, difficult-to-treat form of acute myeloid leukemia (AML).

"These results mark a new era in understanding and treatment of AEL, an aggressive leukemia that has been plagued by diagnostic controversy and poor outcomes," senior author Charles Mullighan, a pathology researcher and co-leader of the St. Jude Children's Research Hospital's hematological malignancies program, said in a statement. 

As they reported online today in Nature Genetics, Mullighan and colleagues performed whole-genome, exome, targeted, and transcriptome sequencing on samples from 159 pediatric or adult AEL patients treated at sites around the world, comparing the somatic mutations and gene expression patterns they found to those in samples from more than 1,900 individuals with non-AEL conditions — from other forms of AML to myelodysplastic syndrome.

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Cancers are tissue-specific, truly important perspective

Tissue-specificity in cancer: The rule, not the exception

Kevin M. Haigis, Karen Cichowski, and Stephen J. Elledge

Science (article)

“Abstract—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”

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Lucence improving personalized liver cancer treatment with AI

Lucence Diagnostics to Develop AI Tools for Liver Cancer Treatment

Lucence Diagnostics, a genomic medicine company focused on personalizing cancer care, today announced a new project to develop AI algorithms for improving diagnosis and treatment of liver cancer. The goal is to combine the imaging and molecular data from liver cancer patients into smarter software tools that help physicians make better treatment decisions.

Lucence will be working with Olivier Gevaert, PhD, Assistant Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science at the Stanford University School of Medicine. Having developed LiquidHALLMARK®, the world's first liquid biopsy next-generation sequencing test that analyzes the DNA of cancer-causing mutations and viruses, Lucence will contribute its genomics expertise and proprietary sequencing technology to this project.

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Cloud-based access to the fully sequenced genomes of 10,000 pediatric patients with cancer

Genomics Data Could Lead to New Treatments for Children

BY BETH FAND INCOLLINGO

PUBLISHED MARCH 12, 2019

St. Jude Children’s Research Hospital is offering cloud-based access to the fully sequenced genomes of 10,000 pediatric patients with cancer, in the hopes that sharing the information will lead to the highest possible number of treatment breakthroughs.

Called the Pediatric Cancer Genome Project (PCGP), the growing set of data, categorized by cancer type, is meant to help researchers at the Memphis facility and beyond understand the genetic mutations that drive pediatric cancers and find new drugs to treat the diseases.

In whole-genome sequencing, a child’s normal and tumor genes are sequenced and then compared. Mutations that are present in a child’s tumor but not his or her normal genes may be driving the disease, and could be good candidates to target with drugs, said Jinghui Zang, Ph.D., chair of the Department of Computational Biology at St. Jude.

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Cancer mutation characterization with machine learning (original article -- very cool)

Integrated structural variation and point mutation signatures in cancer genomes using correlated topic models

Loss of DNA repair mechanisms can leave specific mutation signatures in the genomes of cancer cells. To identify cancers with broken DNA-repair processes, accurate methods are needed for detecting mutation signatures and, in particular, their activities or probabilities within individual cancers. In this paper, we introduce a class of statistical modeling methods used for natural language processing, known as “topic models”, that outperform standard methods for signature analysis. We show that topic models that incorporate signature probability correlations across cancers perform best, while jointly analyzing multiple mutation types improves robustness to low mutation counts.

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