AGBT: Focus on Cancer Genomics

As usual, the quality of the scientific presentations at this meeting has been outstanding. The weather, too, has improved at last:


There are too many to cover (or even attend) completely, but one area of interest with a strong focus this year is cancer genomics. Yesterday during plenary sessions, Stacey Gabriel of the Broad Institute of MIT and Harvard presented sequencing of multiple myeloma, a liquid tumor affecting 50,000 people in the US. Around 5,200 gigabases of sequence was generated across 26 tumor samples and matched controls, yielding ~30x average depth per genome. Their mutation detection pipeline achieved an admirable validation rate for somatic SNVs (95%). Short indels were more challenging (~50% validated), and candidate rearrangements even more so (30-50% validated). However, their study validated ~40 somatic mutations per tumor, implicating known MM genes (NRAS, KRAS, TP53) as well as novel ones (DIS3, FAM46C).

Elliott Margulies on Melanoma

Last night, there was a concurrent session devoted to cancer genomics. Eliott Margulies (NIH/NHGRI) led the lineup with his work sequencing the tumor genome and matched normal of a melanoma patient. Using the Illumina platform (2×100 bp), his group achieved 36x and 43x haploid coverage for tumor and normal, respectively, with ~99% of the genome covered by at least one read. Much of the talk was devoted to their analysis pipeline, summarized as:

  1. Initial alignment of Illumina reads with ELAND
  2. Partitioning the reads into “genome” bins of several kilobases
  3. Local realignment with cross_match in highly parallelized fashion
  4. SNV calling with their “Most Probable Genotype” (MPG) method
  5. Removal of variants with any evidence in the Germline, or ones in dbSNP

The 175,768 novel tumor-specific SNVs were classified as coding (807) or noncoding (174,961). Some 513 of 807 coding variants were nonsynonymous. Of these, 101 were selected for validation; 84 got validation results and 75 somatic coding mutations (89%) were confirmed. Unsurprisingly, Dr. Margulies used his group’s expertise in comparative genomics to closely examine the noncoding variants as well. His group recently annotated “Chai” regions of the human genome, which bear evidence of evolutionary constraint that suggest functional relevance. Some 10,285 of the 174,961 fell within Chai regions, and among them were ~2,000 variants predicted to dramatically alter the local structure of DNA (suggesting regulatory changes).

Sequencing Pre- and Post-Treatment Lung Cancer

Ian Bosdet of BC Cancer Agency presented some very interesting work on mutational profiling of pre- and post-treatment lung cancer tumors. His group had the opportunity to participate in a clinical trial at BCCA in which carefully-selected, treatment-naive NSCLC patients underwent a standard therapeutic program. First, each patient underwent a pre-treatment evaluation and biopsy. Next, they received erlotinib (an EGFR inhibitor) until the disease inevitably progressed. Then, another biopsy that was sent for pathology review, as well as DNA/RNA extraction for sequencing. Transcriptome sequencing yielded some interesting findings. For example, the expression of one gene (IER5L or IER5C, it’s hard to read my own handwriting) was highly expressed in smokers that did not respond to treatment. A screen of unmapped transcript reads against viral genomes revealed the presence of Epstein-Barr Virus transcripts in one tumor that was later re-classified as EBV-positive lymphadenocarcinoma (?).

Mutational profiling for three patients was obtained via exome capture (Agilent) and sequencing of normal, pre-treatment tumor, and post-treatment tumor samples. Somatic mutations in PHACTR2 were seen only in pre-treatment samples. Mutations in a few genes (PRMT10, RanBP2) were found at both times, but a few (YY1AP1, SNX9) were only present after treatment, suggesting a role for these genes in progressive disease.

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