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PubMed: 19789704    PubMedCentral: PMC2749210

Analysis of cancer mutation signatures in blood by a novel ultra-sensitive assay: monitoring of therapy or recurrence in non-metastatic breast cancer.

Chen Z, Feng J, Buzin CH, Liu Q, Weiss L, Kernstine K, Somlo G, Sommer SS
PloS one, , 2009


BACKGROUND: Tumor DNA has been shown to be present both in circulating tumor cells in blood and as fragments in the plasma of metastatic cancer patients. The identification of ultra-rare tumor-specific mutations in blood would be the ultimate marker to measure efficacy of cancer therapy and/or early recurrence. Herein we present a method for detecting microinsertions/deletions/indels (MIDIs) at ultra-high analytical selectivity. MIDIs comprise about 15% of mutations. METHODS AND FINDINGS: We describe MIDI-Activated Pyrophosphorolysis (MAP), a method of ultra-high analytical selectivity for detecting MIDIs. The high analytical selectivity of MAP is putatively due to serial coupling of two rare events: heteroduplex slippage and mis-pyrophosphorolysis. MAP generally has an analytical selectivity of one mutant molecule per >1 billion wild type molecules and an analytical sensitivity of one mutant molecule per reaction. The analytical selectivity of MAP is about 100,000-fold better than that of our previously described method of Pyrophosphorolysis Activated Polymerization-Allele specific amplification (PAP-A) for detecting MIDIs. The utility of this method is illustrated in two ways. 1) We demonstrate that two EGFR deletions commonly found in lung cancers are not present in tissue from four normal human lungs (10(7) copies of gDNA each) or in blood samples from 10 healthy individuals (10(7) copies of gDNA each). This is inconsistent, at least at an analytical sensitivity of 10(-7), with the hypotheses of (a) hypermutation or (b) strong selection of these growth factor-mutated cells during normal lung development leads to accumulation of pre-neoplastic cells with these EGFR mutations, which sometimes can lead to lung cancer in late adulthood. Moreover, MAP was used for large scale, high throughput "gene pool" analysis. No germline or early embryonic somatic mosaic mutation was detected (at a frequency of >0.3%) for the 15/18 bp EGFR deletion mutations in 6,400 individuals, suggesting that early embryonic EGFR somatic mutation is very rare, inconsistent with hypermutation or strong selection of these deletions in the embryo. 2) The second illustration of MAP utility is in personalized monitoring of therapy and early recurrence in cancer. Tumor-specific p53 mutations identified at diagnosis in the plasma of six patients with stage II and III breast cancer were undetectable after therapy in four women, consistent with clinical remission, and continued to be detected after treatment in two others, reflecting tumor progression. CONCLUSIONS: MAP has an analytical selectivity of one part per billion for detection of MIDIs and an analytical sensitivity of one molecule. MAP provides a general tool for monitoring ultra-rare mutations in tissues and blood. As an example, we show that the personalized cancer signature in six out of six patients with non-metastatic breast cancer can be detected and that levels over time are correlated with the clinical course of disease.

Organism/Genes in external databases

Datasource Data
Species found in fulltext (Linnaeus)
Homo sapiens
Rattus norvegicus
Mus musculus
Genes found in fulltext (GNAT)

Best predicted genome from sequences: Homo sapiens

Best predicted genes based on DNA sequences found in paper:

Symbol Ensembl Sequences
EGFR ENSG00000146648 1,5,7,9,11,15,16,18,40,41,43,46,47,49,53,55,82
ERBB2 ENSG00000141736 20,51,74
TP53 ENSG00000141510 30,31,32,35,36,38
AC006977.1 ENSG00000224057 69,70

Genome Annotation: Links to best and chained genome matches

SeqNo Coordinate Range
30, 31, 32, 35, 37, 38 chr17:7577385-7579547
0, 1, 5, 7, 9, 11, 14, 15, 16, 17, 18, 19, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 53, 54, 55, 65, 66, 67, 69, 70, 72, 82 chr7:55211031-55259679
20, 51, 74 chr17:37880176-37880243

Recognized sequences in fulltext

SeqNo file name Recognized DNA
0 PMC2749210.S1.xls tcccacagccccagtgtccctcaccttcgg
1 PMC2749210.S1.xls ggatttccttgttggctttcggagatgttt
2 PMC2749210.S1.xls tcccacagccccagtgtccctcaccttcgg
3 PMC2749210.S1.xls gatttccttgttggctttcggagatgtttt
4 PMC2749210.S1.xls tcccacagccccagtgtccctcaccttcgg
5 PMC2749210.S1.xls atttccttgttggctttcggagatgttttt
6 PMC2749210.S1.xls tcccacagccccagtgtccctcaccttcgg
7 PMC2749210.S1.xls ttccttgttggctttcggagatgtttttat
8 PMC2749210.S1.xls tcccacagccccagtgtccctcaccttcgg
9 PMC2749210.S1.xls ccttgttggctttcggagatgtttttatag
10 PMC2749210.S1.xls tcccacagccccagtgtccctcaccttcgg
11 PMC2749210.S1.xls cttgttggctttcggagatgtttttatagc
12 PMC2749210.S1.xls tcccacagccccagtgtccctcaccttcgg
13 PMC2749210.S1.xls ttgttggctttcggagatgtttttatagcg
14 PMC2749210.S1.xls ctccacagccccagtgtccctcaccttc
15 PMC2749210.S1.xls tcgaggatttccttgttggctttcgattc
16 PMC2749210.S1.xls tcaacacagtggagcgaattcctttgcatc
17 PMC2749210.S1.xls ctccgaggtggaattgagtgacaagctcgc
18 PMC2749210.S1.xls atgaactacttggaggaccgtcgcttagcc
19 PMC2749210.S1.xls ctaaagccacctccttactttgcctccttc
20 PMC2749210.S1.xls tgtgaaaattccagtggccatcaacacatc
21 PMC2749210.S1.xls gaggtggaggttgcagtgagccgagattaac
22 PMC2749210.S1.xls gaaagtgaaaatccctgtggccatcaacac
23 PMC2749210.S1.xls ttaagacacaaactaaggaagcaagactgac
24 PMC2749210.S1.xls tgaaaatccctgtggccatcaaggagtcc
25 PMC2749210.S1.xls ttaagacacaaactaaggaagcaagactgac
26 PMC2749210.S1.xls caggaaactgaggtgaggcatggtgagggc
27 PMC2749210.S1.xls atccgcatctgagcctggttgggcattagc
28 PMC2749210.S1.xls gaatgtgaaaatccccgtggctatcaacac
29 PMC2749210.S1.xls agggggtcttctcacattcccaagggaggc
30 PMC2749210.S1.xls cggacgatattgaacaatggttcacaagac
31 PMC2749210.S1.xls gagcagcctctggcattctgggagcttc
32 PMC2749210.S1.xls ggaaatttgcgtgtggagtatttggatgac
33 PMC2749210.S1.xls aggcggctcatagggcaccaccacactgtc
34 PMC2749210.S1.xls ccactacaactacatgtgtaacagttctgc
35 PMC2749210.S1.xls tgatggtgaggatgggcctccggttcatgc
36 PMC2749210.S1.xls atccactacaactacatgtgtaacagtagttc
37 PMC2749210.S1.xls ggtggatgggtagtagtatggaagaaatc
38 PMC2749210.S1.xls aagacccaggtccagatgaagctc
39 PMC2749210.S1.xls atttgtccttccaaatgagctggcaagtgc
40 PMC2749210.S1.xls ttatacaccgtgccgaacgcaccggagcaa
41 PMC2749210.S1.xls aaagttaaaattcccgtcgctatcaaggaaca
42 PMC2749210.S1.xls agatgagcagggtctagagcagagcagctg
43 PMC2749210.S1.xls agttaaaattcccgtcgctatcaaggttgc
44 PMC2749210.S1.xls gtgctgtctctaaggggagggagttatacc
45 PMC2749210.S1.xls tggcatgaacatgaccctgaattcggatgc
46 PMC2749210.S1.xls ctttctcttccgcacccagcagtatggccc
47 PMC2749210.S1.xls ccctcaacacagtggagcgaattcctttgcatcagaggaaatatg
48 PMC2749210.S1.xls ccctgcagtatcttacaca
49 PMC2749210.S1.xls agggcatgaactacttggaggaccgtcgcttagccaggaacgtac
50 PMC2749210.S1.xls gaatgtctggagagcatcct
51 PMC2749210.S1.xls gggagaatgtgaaaattccagtggccatcaacacatcccccaaag
52 PMC2749210.S1.xls ggcatgaaaatcgcttgagtc
53 PMC2749210.S1.xls aaagttaaaattcccgtcgctatcaaggaacagaaagccaacaagg
54 PMC2749210.S1.xls aggccagtgctgtctctaag
55 PMC2749210.S1.xls aaagttaaaattcccgtcgctatcaaggttgcttctccgaaagcc
56 PMC2749210.S1.xls aggccagtgctgtctctaag
57 PMC2749210.S1.xls cgagaaagtgaaaatccctgtggccatcaacacatctcccaaagc
58 PMC2749210.S1.xls ccaaggtcccccaaagcagat
59 PMC2749210.S1.xls aagtgaaaatccctgtggccatcaaggagtccaaagccaacaagg
60 PMC2749210.S1.xls ccaaggtcccccaaagcagat
61 PMC2749210.S1.xls ctgtagtgggcgtcctgctgt
62 PMC2749210.S1.xls atccgcatctgagcctggttgggcattagcggctccactaactga
63 PMC2749210.S1.xls ggagaatgtgaaaatccccgtggctatcaacacatctcctaaagc
64 PMC2749210.S1.xls agacagtgaaggaggggaagg
65 PMC2749210.S1.xls gcgtggaaacagacataga
66 PMC2749210.S1.xls taacttgggaaaaacactgg
67 PMC2749210.S1.xls tgtgattcgtggagcccaac
68 PMC2749210.S1.xls aggccagtgctgtctctaag
69 PMC2749210.S1.xls acttcacagccctgcgtaa
70 PMC2749210.S1.xls tatccccatggcaaactctt
71 PMC2749210.S1.xls ccctgcagtatcttacaca
72 PMC2749210.S1.xls tggatcagtagtcactaacgt
73 PMC2749210.S1.xls gaatgtctggagagcatcct
74 PMC2749210.S1.xls cctgatggggagaatgtgaaa
75 PMC2749210.S1.xls ggcatgaaaatcgcttgagtc
76 PMC2749210.S1.xls tatcgggctcacaaggcaaca
77 PMC2749210.S1.xls ccaaggtcccccaaagcagat
78 PMC2749210.S1.xls ctgtagtgggcgtcctgctgt
79 PMC2749210.S1.xls gaccctgcccctcccttaga
80 PMC2749210.S1.xls gaggggcagggtcaagaga
81 PMC2749210.S1.xls agacagtgaaggaggggaagg
82 PMC2749210.S7.doc atcaaggaattaagagaagcaacatctccgaaagccaactccctcaccttcgg
Display recognized sequences in FASTA format