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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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FFPE and frozen data yield comparable alteration dataFFPE and frozen tissue were extracted from identical tumor samples and analyzed for cross-validation of mutations where there was sufficient power to detect the mutation in the validation sample (A). FFPE to frozen and frozen to FFPE validation rates binned by allelic fractions demonstrate similar validation and false positive rates between the two groups (B–C). Copy number profiles derived from exomes of the same tumor in either FFPE or frozen tissue yield comparable results (R2 (Pearson) = 0.89; P < 0.001) (D–E). When comparing the FFPE and frozen segment means for all exons across 11 patients, the R2 (Pearson) = 0.79 (P < 0.001) (F).
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Figure 2: FFPE and frozen data yield comparable alteration dataFFPE and frozen tissue were extracted from identical tumor samples and analyzed for cross-validation of mutations where there was sufficient power to detect the mutation in the validation sample (A). FFPE to frozen and frozen to FFPE validation rates binned by allelic fractions demonstrate similar validation and false positive rates between the two groups (B–C). Copy number profiles derived from exomes of the same tumor in either FFPE or frozen tissue yield comparable results (R2 (Pearson) = 0.89; P < 0.001) (D–E). When comparing the FFPE and frozen segment means for all exons across 11 patients, the R2 (Pearson) = 0.79 (P < 0.001) (F).

Mentions: Next, we sought to compare WES data generated from FFPE and frozen material. We assessed WES data from 11 lung adenocarcinomas for which tumor and adjacent normal tissue were available from matched FFPE (aged ≤ 5 years, Supplementary Table 3, Supplementary Figs 1–2) and frozen samples (Fig. 2A). First, we applied our standard mutation detection pipeline on the tumor-normal pairs (Methods) and considered the concordance of mutation calls observed in FFPE tumors that were observed in frozen tumors, and vice versa. We did not expect identical data given tumor heterogeneity15 and nucleotide transition artifacts induced by FFPE fixation16–18. Moreover, the mean target coverage achieved for the FFPE tumor and adjacent tissue samples were 1.5–2 times that for the corresponding FF samples (Supplementary Fig. 3); as a result, we had increased power to detect mutations in FFPE samples compared to the FF samples19. Therefore, we considered the subset of observed exonic mutations in FFPE cases where the depth of coverage afforded sufficient power (> 95%) to detect the mutation in ≥ 2 reads in the matched frozen case, and vice versa. For sufficiently powered sites, 91.5% (2923/3194, 95% confidence interval (CI) ± 0.97) of FFPE mutations validated in patient-matched frozen samples. Similarly, 91.0% (3399/3735, 95% CI ± 0.92) frozen mutations validated in sufficiently powered FFPE samples (P = 0.47) (Fig. 2A–C, Supplementary Table 4). Since the mean target coverage in the FFPE cases were higher than their FF counterparts, we then obtained a random subset of reads from each case such that all sites had a maximum coverage of 90X (“downsampling”19) and repeated the cross-validation exercise. In this scenario, our FFPE to FF and FF to FFPE validation rates for sufficiently powered sites were 92.6% (2811/3036, 95% CI ± 0.93) and 91.5% (3340/3651, 95% CI ± 0.90), respectively (Supplementary Fig. 4A–B, Supplementary Table 4).


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)

FFPE and frozen data yield comparable alteration dataFFPE and frozen tissue were extracted from identical tumor samples and analyzed for cross-validation of mutations where there was sufficient power to detect the mutation in the validation sample (A). FFPE to frozen and frozen to FFPE validation rates binned by allelic fractions demonstrate similar validation and false positive rates between the two groups (B–C). Copy number profiles derived from exomes of the same tumor in either FFPE or frozen tissue yield comparable results (R2 (Pearson) = 0.89; P < 0.001) (D–E). When comparing the FFPE and frozen segment means for all exons across 11 patients, the R2 (Pearson) = 0.79 (P < 0.001) (F).
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Related In: Results  -  Collection

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Figure 2: FFPE and frozen data yield comparable alteration dataFFPE and frozen tissue were extracted from identical tumor samples and analyzed for cross-validation of mutations where there was sufficient power to detect the mutation in the validation sample (A). FFPE to frozen and frozen to FFPE validation rates binned by allelic fractions demonstrate similar validation and false positive rates between the two groups (B–C). Copy number profiles derived from exomes of the same tumor in either FFPE or frozen tissue yield comparable results (R2 (Pearson) = 0.89; P < 0.001) (D–E). When comparing the FFPE and frozen segment means for all exons across 11 patients, the R2 (Pearson) = 0.79 (P < 0.001) (F).
Mentions: Next, we sought to compare WES data generated from FFPE and frozen material. We assessed WES data from 11 lung adenocarcinomas for which tumor and adjacent normal tissue were available from matched FFPE (aged ≤ 5 years, Supplementary Table 3, Supplementary Figs 1–2) and frozen samples (Fig. 2A). First, we applied our standard mutation detection pipeline on the tumor-normal pairs (Methods) and considered the concordance of mutation calls observed in FFPE tumors that were observed in frozen tumors, and vice versa. We did not expect identical data given tumor heterogeneity15 and nucleotide transition artifacts induced by FFPE fixation16–18. Moreover, the mean target coverage achieved for the FFPE tumor and adjacent tissue samples were 1.5–2 times that for the corresponding FF samples (Supplementary Fig. 3); as a result, we had increased power to detect mutations in FFPE samples compared to the FF samples19. Therefore, we considered the subset of observed exonic mutations in FFPE cases where the depth of coverage afforded sufficient power (> 95%) to detect the mutation in ≥ 2 reads in the matched frozen case, and vice versa. For sufficiently powered sites, 91.5% (2923/3194, 95% confidence interval (CI) ± 0.97) of FFPE mutations validated in patient-matched frozen samples. Similarly, 91.0% (3399/3735, 95% CI ± 0.92) frozen mutations validated in sufficiently powered FFPE samples (P = 0.47) (Fig. 2A–C, Supplementary Table 4). Since the mean target coverage in the FFPE cases were higher than their FF counterparts, we then obtained a random subset of reads from each case such that all sites had a maximum coverage of 90X (“downsampling”19) and repeated the cross-validation exercise. In this scenario, our FFPE to FF and FF to FFPE validation rates for sufficiently powered sites were 92.6% (2811/3036, 95% CI ± 0.93) and 91.5% (3340/3651, 95% CI ± 0.90), respectively (Supplementary Fig. 4A–B, Supplementary Table 4).

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