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Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine.

Van Allen EM, Wagle N, Stojanov P, Perrin DL, Cibulskis K, Marlow S, Jane-Valbuena J, Friedrich DC, Kryukov G, Carter SL, McKenna A, Sivachenko A, Rosenberg M, Kiezun A, Voet D, Lawrence M, Lichtenstein LT, Gentry JG, Huang FW, Fostel J, Farlow D, Barbie D, Gandhi L, Lander ES, Gray SW, Joffe S, Janne P, Garber J, MacConaill L, Lindeman N, Rollins B, Kantoff P, Fisher SA, Gabriel S, Getz G, Garraway LA - Nat. Med. (2014)

Bottom Line: The platform employs computational methods for effective clinical analysis and interpretation of WES data.When applied retrospectively to 511 exomes, the interpretative framework revealed a 'long tail' of somatic alterations in clinically important genes.Prospective application of this approach identified clinically relevant alterations in 15 out of 16 patients.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA. [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

ABSTRACT
Translating whole-exome sequencing (WES) for prospective clinical use may have an impact on the care of patients with cancer; however, multiple innovations are necessary for clinical implementation. These include rapid and robust WES of DNA derived from formalin-fixed, paraffin-embedded tumor tissue, analytical output similar to data from frozen samples and clinical interpretation of WES data for prospective use. Here, we describe a prospective clinical WES platform for archival formalin-fixed, paraffin-embedded tumor samples. The platform employs computational methods for effective clinical analysis and interpretation of WES data. When applied retrospectively to 511 exomes, the interpretative framework revealed a 'long tail' of somatic alterations in clinically important genes. Prospective application of this approach identified clinically relevant alterations in 15 out of 16 patients. In one patient, previously undetected findings guided clinical trial enrollment, leading to an objective clinical response. Overall, this methodology may inform the widespread implementation of precision cancer medicine.

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PHIAL reveals the “long tail” of clinically relevant eventsPHIAL takes as input somatic alterations and uses heuristics to assign clinical and biological significance to each alteration (A). PHIAL uses the TARGET database, a curated set of genes that are linked to predictive, prognostic, and/or diagnostic clinical actions when somatically altered in cancers. (B). PHIAL utilizes additional rules to maximize exome data for individuals, including knowledge about kinase domains, copy number directionality, and two-hit pathway events (C). The resulting data is visualized for individual or cohort-level information with this demonstrative PHIAL “gel”. Each alteration is a point sorted by PHIAL score (top are of highest clinical relevance), color coded by potential clinical relevance (red), biological relevance (orange), pathway relevance (yellow), or synonymous variants (gray) (D). A PHIAL “gel” for 511 patient exomes spanning six different disease types (n = 258,226 total somatic alterations). The size of the point is proportional to the number of times a given gene arises at that PHIAL score level. (E). This approach highlights the “long tail” of potentially clinically relevant alterations in TARGET genes (n = 121) that may be present in an individual patient but does not occur sufficiently to be labeled a biological driver across a cohort. The majority of events occur in genes that individually are altered in less than 2% of the overall cohort (F). New cancer clinical trials with TARGET genes specifically integrated into the study per ClinicalTrials.gov over a seven-year period (G).
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Figure 3: PHIAL reveals the “long tail” of clinically relevant eventsPHIAL takes as input somatic alterations and uses heuristics to assign clinical and biological significance to each alteration (A). PHIAL uses the TARGET database, a curated set of genes that are linked to predictive, prognostic, and/or diagnostic clinical actions when somatically altered in cancers. (B). PHIAL utilizes additional rules to maximize exome data for individuals, including knowledge about kinase domains, copy number directionality, and two-hit pathway events (C). The resulting data is visualized for individual or cohort-level information with this demonstrative PHIAL “gel”. Each alteration is a point sorted by PHIAL score (top are of highest clinical relevance), color coded by potential clinical relevance (red), biological relevance (orange), pathway relevance (yellow), or synonymous variants (gray) (D). A PHIAL “gel” for 511 patient exomes spanning six different disease types (n = 258,226 total somatic alterations). The size of the point is proportional to the number of times a given gene arises at that PHIAL score level. (E). This approach highlights the “long tail” of potentially clinically relevant alterations in TARGET genes (n = 121) that may be present in an individual patient but does not occur sufficiently to be labeled a biological driver across a cohort. The majority of events occur in genes that individually are altered in less than 2% of the overall cohort (F). New cancer clinical trials with TARGET genes specifically integrated into the study per ClinicalTrials.gov over a seven-year period (G).

Mentions: Having demonstrated robust WES using FFPE-derived tumor DNA, we next sought to integrate this methodology into a broader framework for clinical interpretation of somatic alterations. We reasoned that a heuristic (rule-based) approach that incorporated prior clinical and scientific knowledge might offer a useful set of organizing principles. By utilizing primary literature, manual curation, and expert opinion, we generated a database of tumor alterations relevant for genomics-driven therapy (TARGET), a database of genes that may have therapeutic, prognostic, and diagnostic implications for cancer patients (Fig. 3B, Supplementary Table 5, Methods). We integrated the resulting 121 TARGET genes with existing open-source resources to create a series of rules that: (i) sort each somatic variant by clinical and biological relevance; (ii) link TARGET genes with additional biologically significant pathways and gene sets; and (iii) demote variants of uncertain significance. Thus, the resulting analytical algorithm used precision heuristics for interpreting the alteration landscape (PHIAL) (Fig. 3A–D, Methods). Beyond annotating variants, PHIAL applies rules that rank variants based on clinical and biological relevance to computationally sort a patient’s somatic variants.


Whole-exome sequencing and clinical interpretation of formalin-fixed, paraffin-embedded tumor samples to guide precision cancer medicine.

Van Allen EM, Wagle N, Stojanov P, Perrin DL, Cibulskis K, Marlow S, Jane-Valbuena J, Friedrich DC, Kryukov G, Carter SL, McKenna A, Sivachenko A, Rosenberg M, Kiezun A, Voet D, Lawrence M, Lichtenstein LT, Gentry JG, Huang FW, Fostel J, Farlow D, Barbie D, Gandhi L, Lander ES, Gray SW, Joffe S, Janne P, Garber J, MacConaill L, Lindeman N, Rollins B, Kantoff P, Fisher SA, Gabriel S, Getz G, Garraway LA - Nat. Med. (2014)

PHIAL reveals the “long tail” of clinically relevant eventsPHIAL takes as input somatic alterations and uses heuristics to assign clinical and biological significance to each alteration (A). PHIAL uses the TARGET database, a curated set of genes that are linked to predictive, prognostic, and/or diagnostic clinical actions when somatically altered in cancers. (B). PHIAL utilizes additional rules to maximize exome data for individuals, including knowledge about kinase domains, copy number directionality, and two-hit pathway events (C). The resulting data is visualized for individual or cohort-level information with this demonstrative PHIAL “gel”. Each alteration is a point sorted by PHIAL score (top are of highest clinical relevance), color coded by potential clinical relevance (red), biological relevance (orange), pathway relevance (yellow), or synonymous variants (gray) (D). A PHIAL “gel” for 511 patient exomes spanning six different disease types (n = 258,226 total somatic alterations). The size of the point is proportional to the number of times a given gene arises at that PHIAL score level. (E). This approach highlights the “long tail” of potentially clinically relevant alterations in TARGET genes (n = 121) that may be present in an individual patient but does not occur sufficiently to be labeled a biological driver across a cohort. The majority of events occur in genes that individually are altered in less than 2% of the overall cohort (F). New cancer clinical trials with TARGET genes specifically integrated into the study per ClinicalTrials.gov over a seven-year period (G).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4048335&req=5

Figure 3: PHIAL reveals the “long tail” of clinically relevant eventsPHIAL takes as input somatic alterations and uses heuristics to assign clinical and biological significance to each alteration (A). PHIAL uses the TARGET database, a curated set of genes that are linked to predictive, prognostic, and/or diagnostic clinical actions when somatically altered in cancers. (B). PHIAL utilizes additional rules to maximize exome data for individuals, including knowledge about kinase domains, copy number directionality, and two-hit pathway events (C). The resulting data is visualized for individual or cohort-level information with this demonstrative PHIAL “gel”. Each alteration is a point sorted by PHIAL score (top are of highest clinical relevance), color coded by potential clinical relevance (red), biological relevance (orange), pathway relevance (yellow), or synonymous variants (gray) (D). A PHIAL “gel” for 511 patient exomes spanning six different disease types (n = 258,226 total somatic alterations). The size of the point is proportional to the number of times a given gene arises at that PHIAL score level. (E). This approach highlights the “long tail” of potentially clinically relevant alterations in TARGET genes (n = 121) that may be present in an individual patient but does not occur sufficiently to be labeled a biological driver across a cohort. The majority of events occur in genes that individually are altered in less than 2% of the overall cohort (F). New cancer clinical trials with TARGET genes specifically integrated into the study per ClinicalTrials.gov over a seven-year period (G).
Mentions: Having demonstrated robust WES using FFPE-derived tumor DNA, we next sought to integrate this methodology into a broader framework for clinical interpretation of somatic alterations. We reasoned that a heuristic (rule-based) approach that incorporated prior clinical and scientific knowledge might offer a useful set of organizing principles. By utilizing primary literature, manual curation, and expert opinion, we generated a database of tumor alterations relevant for genomics-driven therapy (TARGET), a database of genes that may have therapeutic, prognostic, and diagnostic implications for cancer patients (Fig. 3B, Supplementary Table 5, Methods). We integrated the resulting 121 TARGET genes with existing open-source resources to create a series of rules that: (i) sort each somatic variant by clinical and biological relevance; (ii) link TARGET genes with additional biologically significant pathways and gene sets; and (iii) demote variants of uncertain significance. Thus, the resulting analytical algorithm used precision heuristics for interpreting the alteration landscape (PHIAL) (Fig. 3A–D, Methods). Beyond annotating variants, PHIAL applies rules that rank variants based on clinical and biological relevance to computationally sort a patient’s somatic variants.

Bottom Line: The platform employs computational methods for effective clinical analysis and interpretation of WES data.When applied retrospectively to 511 exomes, the interpretative framework revealed a 'long tail' of somatic alterations in clinically important genes.Prospective application of this approach identified clinically relevant alterations in 15 out of 16 patients.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA. [2] Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.

ABSTRACT
Translating whole-exome sequencing (WES) for prospective clinical use may have an impact on the care of patients with cancer; however, multiple innovations are necessary for clinical implementation. These include rapid and robust WES of DNA derived from formalin-fixed, paraffin-embedded tumor tissue, analytical output similar to data from frozen samples and clinical interpretation of WES data for prospective use. Here, we describe a prospective clinical WES platform for archival formalin-fixed, paraffin-embedded tumor samples. The platform employs computational methods for effective clinical analysis and interpretation of WES data. When applied retrospectively to 511 exomes, the interpretative framework revealed a 'long tail' of somatic alterations in clinically important genes. Prospective application of this approach identified clinically relevant alterations in 15 out of 16 patients. In one patient, previously undetected findings guided clinical trial enrollment, leading to an objective clinical response. Overall, this methodology may inform the widespread implementation of precision cancer medicine.

Show MeSH
Related in: MedlinePlus