WES of FFPE samples

To produce WES data for clinical use, robust sequencing data must frequently be generated from small quantities of archival FFPE tissue. To test this, we extracted DNA from 99 FFPE samples using the FFPE extraction protocol (Supplementary Table 1 and Online Methods). A comparison of standard WES metrics14 with 768 non-FFPE samples (394 whole blood, 367 frozen, 7 cell line) sequenced in parallel demonstrated no significant differences independent of input DNA quantity (P > 0.05, Mann-Whitney U-test; Fig. 1a–c and Supplementary Table 1). Our lowest successful WES attempts were achieved with 13.6 ng and 16 ng DNA derived from non-FFPE and FFPE tissue, respectively.

Figure 1: FFPE and frozen sample sequencing metrics. (a–c) The percentage of target bases covered at 20× (a), percentage of selected bases (b) and percentage of zero-coverage targets (c) in FFPE (n = 99) and non-FFPE (n = 768) tissue. Additional quality control metrics for all 867 cases are available in Supplementary Table 1. No statistically significant difference between FFPE and non-FFPE tissue was observed in these three metrics (P > 0.05; two-sided Mann-Whitney U-test). Full size image Download Excel source data

Moreover, improvements in process design (Online Methods) combined with the 'with-bead' approach14 yielded a time to exome data delivery of 17.4 ± 2.2 d (median ± s.d.; 25th and 75th percentiles 14.3 and 18.6, respectively) for FFPE samples received as DNA and 20.1 ± 2.4 d (median ± s.d.; 25th and 75th percentiles 17.5 and 21.2, respectively) for samples received as FFPE tissue blocks (Supplementary Table 2). This turnaround time is compatible with several clinical oncology applications.

We next assessed WES data using even smaller amounts of input DNA. Here, we achieved >80% of targeted nucleotides from the hybrid selection reaction, even when we used only 1 ng of input DNA; we saw equivalent results with DNA derived from FFPE and non-FFPE tissue. However, to meet our metrics of ≥80% targets with at least 20× coverage and ≥100× mean target coverage across the exome, a disproportionate amount of additional sequencing was required owing to an increase in the fraction of duplicate molecules in the library.

FFPE and fresh frozen samples yield comparable WES results

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 and Supplementary Figs. 1 and 2) and frozen (Fig. 2a) samples. First, we applied our standard mutation detection pipeline on the tumor-normal pairs (Online Methods) and considered the concordance of mutation calls observed in FFPE tumors that we observed in frozen tumors and vice versa. We did not expect identical data, given tumor heterogeneity15 and low allelic fraction nucleotide transition artifacts induced by the FFPE fixation process16,17,18. Moreover, the mean target coverage achieved for the FFPE tumor and adjacent tissue samples was 1.5–2 times that for the corresponding fresh frozen samples (Supplementary Fig. 3); as a result, we had increased power to detect mutations in FFPE samples compared to the fresh frozen samples19. Therefore, we considered the subset of observed exonic mutations in FFPE tumor cases where the depth of coverage afforded sufficient power (>95%) to detect the mutation in two or more reads in the matched frozen tumor case and vice versa. For sufficiently powered sites, 91.5% (2,923/3,194, 95% confidence interval (CI) ± 0.97) of mutations in FFPE samples were validated in patient-matched frozen samples. Similarly, 91.0% (3,399/3,735, 95% CI ± 0.92) frozen mutations were validated in sufficiently powered FFPE samples (P = 0.47) (Fig. 2a–c and Supplementary Table 4). Because the mean target coverage in the FFPE cases was higher than in their fresh frozen counterparts, we then obtained a random subset of reads from each case such that all sites had a maximum coverage of 90× ('downsampling'19) and repeated the cross-validation exercise. In this scenario, our validation rates for FFPE to fresh frozen and fresh frozen to FFPE for sufficiently powered sites were 92.6% (2,811/3,036, 95% CI ± 0.93) and 91.5% (3,340/3,651, 95% CI ± 0.90), respectively (Supplementary Fig. 4a,b and Supplementary Table 4).

Figure 2: FFPE and frozen sample data yield comparable alteration data. (a) FFPE 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. (b,c) Validation rates for FFPE to frozen (b) and frozen to FFPE (c) binned by allelic fractions demonstrate similar validation and false positive rates between the two groups. (d,e) Copy number profiles derived from exomes of the same tumor in either FFPE or frozen tissue (d) yielded comparable results (r2 = 0.89; P < 0.001, Pearson's correlation) (e). (f) When comparing the FFPE and frozen segment means for all exons across 11 patients, the r2 = 0.79 (P < 0.001, Pearson's correlation). CR, copy ratio. Full size image Download Excel source data

In both FFPE and fresh frozen cases from each patient, we observed mutations for which there was insufficient power to detect that mutation in the validation cohort after downsampling (Supplementary Fig. 4c and Supplementary Table 4). Demonstrative examples of mutations in FFPE samples that could not be validated in fresh frozen counterparts are provided in Supplementary Figure 5a–c. Overall, these results suggested that the ability to detect base mutations that were sufficiently powered was equivalent regardless of whether frozen or FFPE tissue–derived genomic DNA was used for WES.

We also examined the chromosomal copy number patterns evident in WES data from frozen and FFPE tumor DNA in the 11 lung adenocarcinomas. In one representative patient, copy ratios for matching exons in FFPE and frozen sample data correlated (r2 = 0.89, P < 0.0001, Pearson's correlation; Fig. 2d,e). This correlation held across all 11 cases, representing 1,338,859 exons (r2 = 0.79, P < 0.0001, Pearson's correlation; Fig. 2f). Thus, WES data obtained from FFPE tumor DNA are comparable to fresh frozen sample WES data and may equally be used to measure global chromosome copy number information.

Clinical analysis and interpretation of WES data

Having demonstrated robust WES using FFPE tumor––derived 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 using 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 patients with cancer (Fig. 3b, Supplementary Table 5 and Online 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 and Online Methods). Beyond annotating variants, PHIAL applies rules that rank variants on the basis of clinical and biological relevance to computationally sort a patient's somatic variants.

Figure 3: PHIAL reveals the long tail of clinically relevant events. (a) PHIAL takes as input somatic alterations and uses heuristics to assign clinical and biological significance to each alteration. (b) 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. COSMIC, Catalogue of Somatic Mutations in Cancer; CGC, Cancer Gene Census. (c) PHIAL utilizes additional rules to maximize exome data for individuals, including knowledge about kinase domains, copy number directionality and two-hit pathway events. (d) The resulting data were 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) and color coded by potential clinical relevance (red), biological relevance (orange), pathway relevance (yellow) or synonymous variants (gray). (e) 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. (f) 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. (g) New cancer clinical trials with TARGET genes specifically integrated into the study per ClinicalTrials.gov over a 7-year period. Full size image Download Excel source data

We assessed the functionality of PHIAL using 511 patient cases from six prior WES studies20,21,22,23,24,25. Analysis tools (Online Methods) yielded 258,226 somatic alterations in protein-coding genes, of which 135,903 were nonsynonymous. Of these, PHIAL identified 1,842 somatic alterations in genes linked to clinical actions (TARGET genes) for 80% (408/511) of the patients (Fig. 3e). Additional descriptive statistics regarding altered genes per patient, stratified by inclusion in databases explored in PHIAL, are available in Supplementary Table 6. PHIAL identified known and highly recurrent actionable findings across this patient cohort. It also revealed a long tail of TARGET gene alterations present in small patient subsets that did not reach statistical significance in the individual cohort studies but may have immediate clinical ramifications for individual patients (Fig. 3f). Specifically, 39% (201/511) of the cases had alterations in at least one TARGET gene that was somatically altered in <2% of the overall cohort. This finding was reminiscent of similar long-tail alteration distributions observed for driver genes in cancer1.

As a major near-term goal of precision cancer medicine is to use genetic information to inform clinical trial enrollment, we also systematically queried ClinicalTrials.gov, a centralized registry of publicly and privately supported clinical studies worldwide, for oncology clinical trials linked to TARGET genes. The number of clinical trials including a TARGET gene in the title, the strictest means of identifying clinical trials with a genomic emphasis, grew steadily between 2005 and 2012 (Fig. 3g).

WES and clinically actionable events across cancers

To pilot prospective sequencing and clinical interpretation, we performed WES and PHIAL in 16 patients with a range of advanced cancers (Fig. 4a). WES data for 3 of these 16 patients predated the WES protocol described herein but were included to assess PHIAL output. WES data from all patients in the rapid sequencing protocol met our quality control parameters irrespective of tissue processing type (Supplementary Table 7). By completion of the pilot period, time from sample receipt through data delivery was 16 d.

Figure 4: Clinically relevant findings from individual patients. (a) PHIAL results for 14 patients with a spectrum of malignancies, highlighting nominated clinically actionable alterations in 13 of 14 patients. Asterisks denote patient sequencing data that predated the rapid WES protocol. (b) Using the level of evidence schematic (Supplementary Table 8), all nominated alterations for patients in this study were manually curated and assigned a level of evidence (Supplementary Table 7). Full size image Download Excel source data

For these 16 patients, PHIAL revealed 29 unique TARGET genes in the 'Investigate Clinical Relevance' category (median 2, range 0–5). Although, by definition, alterations in TARGET genes may have implications for clinical decision making, their actual clinical relevance requires case-by-case evaluation in real time. To facilitate this, we manually curated every alteration ranked as Investigate Clinical Relevance by PHIAL to include up-to-date knowledge from databases, literature and computational algorithms. We generated a standardized, structured annotation for each alteration (Supplementary Note) and assigned a level of evidence to each potential clinical action based on that alteration. These levels of evidence (Supplementary Table 8) included predictive, prognostic and diagnostic categories and encompassed validated indications, preclinical evidence and analytical associations.

Following curation and assignment of levels of evidence, we identified 41 clinically relevant alterations in 15 out of 16 patients. These included standard-of-care findings, such as an EGFRL858R mutation in lung adenocarcinoma linked to epidermal growth factor receptor (EGFR) inhibitors (predictive for US Food and Drug Administration (FDA)-approved therapies, level A), and PIK3CA alterations that are entry criteria for clinical trials (predictive for therapies in clinical trials, level A). 46.3% (19/41) of these alterations were based on preclinical evidence for the association of the alteration with response or resistance to FDA-approved therapies or therapies in clinical trials (level D) (Fig. 4b and Supplementary Table 9).

We identified multiple unexpected clinically relevant findings in genes not well characterized for the corresponding tumor type. For instance, we observed CRKL amplification in a patient with metastatic urothelial carcinoma (Supplementary Fig. 6); this alteration has been predicted to confer resistance to EGFR inhibitors26 and sensitivity to Src inhibitors27 in preclinical studies but had not previously been described in urothelial carcinoma. To accommodate new TARGET genes emerging with future findings, we have made TARGET publically available online (http://www.broadinstitute.org/cancer/cga/target) and encourage community contributions.

The use of WES in clinical decision making

We used the prospective WES framework for clinical decision making in one demonstrative case. A patient with metastatic lung adenocarcinoma underwent standard clinical genetic testing that revealed wild-type EGFR, KRAS (codon 12 and 13) and ALK status. Mass spectrometry testing of 471 alterations in 41 genes5 revealed an STK11 frameshift deletion. We started the patient on carboplatin, paclitaxel and bevacizumab (Fig. 5a). In parallel, we applied the clinical WES platform on the FFPE metastatic tumor sample and germline peripheral blood. PHIAL nominated a KRASA146V mutation as clinically relevant, along with alterations in STK11 (identical to other testing) and ATM (Fig. 5a and Supplementary Table 9). KRASA146V is a known activating mutation, although it is possibly less potent than the codon 12 and 13 mutations28. Although activating KRAS mutations are found in 15–30% of all patients with non–small-cell lung cancer (NSCLC) and commonly in conjunction with STK11 loss29, this specific KRAS alteration has not been reported in NSCLC20,30,31,32. We confirmed KRAS146V using the same FFPE tumor sample in a clinical lab (Online Methods) and then returned the data to the patient's oncologist. After rapidly progressing on combination chemotherapy (Fig. 5b), the patient was enrolled in a phase 1 clinical trial of a cyclin-dependent kinase 4 (CDK4) inhibitor (LY2835219) on the basis of preclinical data (level D) implicating a synthetic lethal relationship between activated KRAS and CDK4 (ref. 33). The patient achieved stable disease (per response evaluation criteria in solid tumors (RECIST) 1.1 criteria; 7.9% reduction in tumor volume compared to baseline) and was on therapy for 16 weeks (Fig. 5b,c). Of note, this was the patient's best and only clinical response to any cancer-directed therapy.

Figure 5: Clinical sequencing informs clinical trial enrollment and experimental discovery. (a) The PHIAL output and treatment course for a patient with metastatic lung adenocarcinoma is shown, with the integration of clinical WES occurring during the patient's first-line therapy allowing subsequent clinical trial enrollment. (b) The patient's time-to-relapse data for the three treatment regimens received. (c) Computed tomography radiographic imaging of a representative metastatic focus for the patient on the CDK4 inhibitor trial after two cycles of therapy (measurement is 1.7 × 1.5 cm for baseline mass and 1.3 × 1.3 cm for 2-month interval scan of the same mass). Per RECIST criteria, overall tumor reduction was 7.9%. (d) For another patient, PHIAL nominated a JAK3 missense mutation, and given its location in the kinase domain near alterations previously defined as activating, was considered to have inferential evidence (level E) for being clinically actionable. (e) The crystal structure of JAK3 highlighting the arginine at residue 870 which directly coordinates the phosphate group of the primary activating tyrosine phosphorylation site. (f) Experimental follow-up of this alteration was performed in a Ba/F3 system compared to wild-type or a known activating JAK3 mutation (A572V). Full size image Download Excel source data

To maximize the potential of clinical WES, we also implemented a procedure to generate experimental evidence for selected level E (inferential association) alterations. An exemplary case involved WES in a patient with metastatic castration-resistant prostate cancer that harbored an R870W missense mutation in the gene encoding Janus kinase 3 (JAK3) (Fig. 5d). Activating mutations in JAK3 have been described in hematological malignancies34, and JAK3 inhibitors are available clinically, including the FDA-approved agent tofacitinib. JAK3R870W has not been previously identified in cancer, and the function of this mutation is unknown.

The crystal structure of JAK3 demonstrates that the arginine at residue 870 directly coordinates the phosphate group of the primary activating tyrosine phosphorylation site (pTyr981)35 (Fig. 5e). This interaction is expected to pull JAK3 into the active conformation. Indeed, residue 870 is conserved as an arginine or lysine in virtually all JAKs. Given the functional importance of this residue, we hypothesized that this alteration could, in principle, be activating. Thus, we categorized this alteration as level E (Supplementary Table 9).

We used a Ba/F3 system to examine the activity of JAK3R870W as compared to wild-type JAK3 and a known activating mutation in JAK3, A572V36. Ba/F3 cells are mouse hematopoietic cells dependent on interleukin-3 (IL-3) for survival. Expression of some oncoproteins substitutes for IL-3 signaling, allowing for the growth of Ba/F3 cells in the absence of IL-3. This system has been used extensively to characterize activating mutants of JAK3 in prior studies36. Ba/F3 cells expressing JAK3R870W did not achieve IL-3–independent growth following complete IL-3 withdrawal, in contrast to cells expressing a known JAK3 activating mutation (JAK3A572V) or those growing in the presence of IL-3 (Fig. 5f). This suggested that JAK3R870W is unlikely to be an activating mutation and that JAK3 inhibitors are unlikely to benefit this patient.