Detection and localization of early- and late-stage cancers using platelet RNA

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I–IV cancer patients and in half of 352 stage I–III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening

A new taxonomy of sublinear right-to-left scanning keyword pattern matching algorithms

A new taxonomy of sublinear (multiple) keyword pattern matching algorithms is presented. Based on an earlier taxonomy by the second and third author, this new taxonomy includes not only suffix-based algorithms, but also factor- and factor oracle-based algorithms. In particular, we show how suffix-based (Commentz-Walter like), factor- and factor oracle-based sublinear keyword pattern matching algorithms can be seen as instantiations of a general sublinear algorithm skeleton. During processing, such algorithms shift or jump through the text in a forward or left-to-right direction, and read backward or right-to-left starting from positions in the text, i.e. they read suffixes of certain prefixes of the text. They use finite automata for efficient computation of string membership in a certain language. In addition, we show shift functions defined for the suffix-based algorithms to be reusable for factor- and factor oracle-based algorithms. The taxonomy is based on deriving the algorithms from a common starting point by adding algorithm and problem details, to arrive at efficient or well-known algorithms. Such a presentation provides correctness arguments for the algorithms as well as clarity on how the algorithms are related to one another. In addition, it is helpful in the construction of a toolkit of the algorithms

A new taxonomy of sublinear keyword pattern matching algorithms

Abstract This paper presents a new taxonomy of sublinear (multiple) keyword pattern matching algorithms. Based on an earlier taxonomy by Watson and Zwaan [WZ96, WZ95], this new taxonomy includes not only suffix-based algorithms related to the Boyer-Moore, Commentz-Walter and Fan-Su algorithms, but factor- and factor oracle-based algorithms such as Backward DAWG Matching and Backward Oracle Matching as well. In particular, we show how suffix-based (Commentz-Walter like), factor- and factor oracle-based sublinear keyword pattern matching algorithms can all be seen as instantiations of a general sublinear algorithm skeleton. In addition, we show all shift functions defined for the suffix-based algorithms to be in principle reusable for factor- and factor oracle-based algorithms. The taxonomy is based on deriving the algorithms from a common starting point by adding algorithm and problem details, in order to arrive at efficient or well-known algorithms. Such a presentation provides correctness arguments for the algorithms as well as clarity on how the algorithms are related to one another. In addition, it is helpful in the construction of a toolkit of the algorithms

Constructing factor oracles

A factor oracle is a data structure for weak factor recognition. It is an automatonbuilt on a string p of length m that is acyclic, recognizes at least all factors of p, has m +1 states which are all final, and has m to 2m -1 transitions. In this paper, we give two alternative algorithms for its construction and prove the constructed automata to be equivalent to the automata constructed by the algorithms in a paper by Allauzen et al. Although these new algorithms are practically inefficient compared to the O(m) algorithm given there, they give more insight into factor oracles. Our first algorithm constructs a factor oracle based on the suffixes of p in a way that is more intuitive. Some of the crucial properties of factor oracles, which in the paper by Allauzen et al. need several lemmas to be proven, are immediately obvious. Another important property however becomes less obvious. A second algorithm gives a clear insight in the relationship between the trie or DAWG (directed acyclic word graph) recognizing the factors of p and the factor oracle recognizing a superset thereof

Detection and localization of early- and late-stage cancers using platelet RNA

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I-IV cancer patients and in half of 352 stage I-III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening

Detection and localization of early- and late-stage cancers using platelet RNA

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I–IV cancer patients and in half of 352 stage I–III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening