Supplemental Material
Supplemental Methods
1)Sequencing:
Hybridization-based capture of 3320 exons from 182 cancer-related genes and 37 introns of 14 genes commonly rearranged in cancer (previous version of the test performed for nine patients) and 3769 exons from 236 cancer-related genes and 47 introns of 19 genes commonly rearranged in cancer (performed for 338 patients) was applied to ≥ 50 ng of DNA extracted from 347 tumor specimens and sequenced to high, uniform coverage with a mean sequencing depth of 714× as previously described35. Consistent median sequencing depth was achieved by processing specimens according to optimized, locked down, standard operating procedures (SOP) on automated liquid handlers in a Clinical Laboratory Improvement Act (CLIA)-certified laboratory as previously described35. Genomic alterations (base substitutions, small indels, rearrangements, copy number alterations) were determined and then reported for these patient samples. Six or seven copy numbers are reported as equivocal and 8 are definitive; for ERBB2, equivocal amplification was 5 to 7 copies; all (equivocal or definitely amplified) were designated as positive for amplification for this study).
182 gene panel list:
236 gene panel list:
ABL1 / BTK / CTNNB1 / FGF23 / IL7R / MLH1 / PDGFRA / SMOAKT1 / CARD11 / DAXX / FGF3 / INHBA / MLL / PDGFRB / SOCS1
AKT2 / CBFB / DDR2 / FGF4 / IRF4 / MLL2 / PDK1 / SOX10
AKT3 / CBL / DNMT3A / FGF6 / IRS2 / MPL / PIK3CA / SOX2
ALK / CCND1 / DOT1L / FGFR1 / JAK1 / MRE11A / PIK3CG / SPEN
APC / CCND2 / EGFR / FGFR2 / JAK2 / MSH2 / PIK3R1 / SPOP
AR / CCND3 / EMSY (C11orf30) / FGFR3 / JAK3 / MSH6 / PIK3R2 / SRC
ARAF / CCNE1 / EP300 / FGFR4 / JUN / MTOR / PPP2R1A / STAG2
ARFRP1 / CD79A / EPHA3 / FLT1 / KAT6A (MYST3) / MUTYH / PRDM1 / STAT4
ARID1A / CD79B / EPHA5 / FLT3 / KDM5A / MYC / PRKAR1A / STK11
ARID2 / CDC73 / EPHB1 / FLT4 / KDM5C / MYCL1 / PRKDC / SUFU
ASXL1 / CDH1 / ERBB2 / FOXL2 / KDM6A / MYCN / PTCH1 / TET2
ATM / CDK12 / ERBB3 / GATA1 / KDR / MYD88 / PTEN / TGFBR2
ATR / CDK4 / ERBB4 / GATA2 / KEAP1 / NF1 / PTPN11 / TNFAIP3
ATRX / CDK6 / ERG / GATA3 / KIT / NF2 / RAD50 / TNFRSF14
AURKA / CDK8 / ESR1 / GID4(C17orf39) / KLHL6 / NFE2L2 / RAD51 / TOP1
AURKB / CDKN1B / EZH2 / GNA11 / KRAS / NFKBIA / RAF1 / TP53
AXL / CDKN2A / FAM123B (WTX) / GNA13 / LRP1B / NKX2-1 / RARA / TSC1
BAP1 / CDKN2B / FAM46C / GNAQ / MAP2K1 / NOTCH1 / RB1 / TSC2
BARD1 / CDKN2C / FANCA / GNAS / MAP2K2 / NOTCH2 / RET / TSHR
BCL2 / CEBPA / FANCC / GPR124 / MAP2K4 / NPM1 / RICTOR / VHL
BCL2L2 / CHEK1 / FANCD2 / GRIN2A / MAP3K1 / NRAS / RNF43 / WISP3
BCL6 / CHEK2 / FANCE / GSK3B / MCL1 / NTRK1 / RPTOR / WT1
BCOR / CIC / FANCF / HGF / MDM2 / NTRK2 / RUNX1 / XPO1
BCORL1 / CREBBP / FANCG / HRAS / MDM4 / NTRK3 / SETD2 / ZNF217
BLM / CRKL / FANCL / IDH1 / MED12 / NUP93 / SF3B1 / ZNF703
BRAF / CRLF2 / FBXW7 / IDH2 / MEF2B / PAK3 / SMAD2
BRCA1 / CSF1R / FGF10 / IGF1R / MEN1 / PALB2 / SMAD4
BRCA2 / CTCF / FGF14 / IKBKE / MET / PAX5 / SMARCA4
BRIP1 / CTNNA1 / FGF19 / IKZF1 / MITF / PBRM1 / SMARCB1
SELECT REARRANGEMENTS
ALK / BCR / BCL2 / BRAF / EGFR / ETV1 / ETV4 / ETV5
ETV6 / EWSR1 / MLL / MYC / NTRK1 / PDGFRA / RAF1 / RARA
RET / ROS1 / TMPRSS2
2)Therapy
Treatment was considered “matched” if at least one agent in the treatment regimen targeted at least one aberration or pathway component harbored in a patient’s molecular profile or a functionally active protein preferentially expressed in the tumor (e.g. estrogen receptor (ER) or HER2, assessed by standard of care testing other than NGS) with a half inhibitory concentration (IC50) in the low nM range. Examples of matched therapy included, but were not limited to: anti-EGFR drugs in the presence of EGFR anomalies, mTOR inhibitors for alterations in the PTEN/PIK3CA/Akt/mTOR pathway, BRAF or MEK inhibitors for RAF or RAS aberrations. More specifically, we defined “matched-direct” if at least one drug directly impacted the gene product of the molecular alteration or a differentially expressed protein [e.g. an EGFR inhibitor in a patient with an EGFR alteration (direct effect on the molecular aberration) or hormonal manipulation in patients with over-expressed estrogen or androgen receptors (proteins preferentially expressed on tumor cells were targeted)]. “Matched-indirect” was the term used for a drug that affects a target removed from the molecular aberration (e.g. mTOR inhibitor administered to patient with a PIK3CA mutation). Matched-direct therapy would include small molecule inhibitors with an IC50 ≤ 100 nM for the target, as well as antibodies whose primary target was the aberrant protein or a differentially expressed protein. Small molecule inhibitors that directly impacted a target, but had an 100 nM < IC50 ≤ 250 nM for that target were considered matched-indirect treatments. Matching designation was confirmed by the senior investigator (RK), who was blinded at the time of designation to the outcomes.
3)Matching Score
It is now well known that advanced tumors have multiple aberrations and that combination therapy is likely to be better than monotherapy. Therefore, an exploratory scoring system (“Matching Score”) was developed that divided the number of matched drugs by the number of aberrations. Under this system, the higher the Matching Score, the better the match. The score for each match (direct or indirect) was assigned a 1; no match was a zero. If a drug directly impacted two targets present in the tumor, a 2 was given (example, a multikinase inhibitor with potent activity against more than one target present in a tumor); if two drugs each impacted a target directly in a patient, a 2 was also given. If two drugs were given that impacted directly (or indirectly) the same target in a patient, the number 2 was still given. The Matching Score was calculated by dividing the number derived from the direct and indirect matches in each tumor (numerator) by the number of aberrations (denominator). For instance, if a patient who tumor harbored six genomic aberrations received two drugs, the Matching Score would be 2/6 or 0.33. The cut-off of 0.2 for the OS analysis was chosen according to the minimum p-value criteria (Mazumdar and Glassman21).
4)Statistical Analysis
1. Patient’s characteristics
Patient characteristics were summarized using descriptive statistics. A diagram displays the data availability for the matched and unmatched patients; patients who were lost to follow up or were still on prior therapy or on observation were considered unevaluable (see Figure 1).
2. Study endpoints and definitions
The following clinical endpoints were considered: (i) rate of [stable disease (SD)≥6 months/partial response (PR)/complete response (CR)]; (ii) progression-free survival (PFS) of the first line of therapy given after molecular profile results (PFS2); (iii) PFS2 versus PFS1 (immediate prior line of therapy), i.e., using patients as their own controls22,23; (iv) percent of patients with a PFS2/PFS1 ratio ≥ 1.322; and (v) overall survival (OS). SD, PR, or CR was determined per the assessment of the treating physician. PFS was defined as the time from the beginning of therapy to progression or the time to last follow up for patients that were progression-free (patients that were progression-free on the date of last follow up were censored on that date). OS was defined as the time from the beginning of therapy to death or last follow-up date for patients who were alive (the latter were censored on that date). The cut-off date for the analysis was April 1st 2015; all patients that were progression-free (for PFS) or alive (for OS) as of date of analysis were censored on that date unless their date of last follow up was earlier, in which case that was the date of censoring.
3. Analyses performed
Whenever appropriate, Chi-Square tests were used to compare categorical variables and the non-parametric Mann Whitney-U test to compare two groups on one continuous variable. Binary logistic regressions were performed for categorical endpoints. PFS and OS were analyzed by the Kaplan-Meier method24 and the log-rank test was used to compare variables. Cox regression models were used as multivariable analysis, when appropriate for survival endpoints. The importance of a prognostic factor was assessed by the Chi-Square and Wald-type test statistics (for the log-rank test and logistic regression/Cox regression models, respectively). The higher the Chi-square or Wald, the stronger the association.
3.1 Variables assessed
The main variables analyzed in this study were “matched versus unmatched”, the primary diagnosis, the number of prior therapy lines (in advanced/metastatic setting), if the therapy was single agent or a combination, the total number of alterations, the presence of metastasis at diagnosis, the presence of metastasis at biopsy date (of tissue used for molecular testing).
3.2 Propensity score for being matched vs. not
Given the retrospective nature of our study, and to account for imbalances between patients who were “matched” versus not, the “propensity” to receive a matched therapy for each patient was determined by using a multivariable logistic regression with “matched or not” as the outcome26–28. Variables included in the final propensity score model were “breast or not” cancers, “gastrointestinal or not” cancers, “skin/melanoma or not” cancers, “first line of treatment or not”, and “number of alterations”. This propensity score was used as a covariate in multivariable models or in the inverse probability of matched treatment weighting method. In the latter method, “matched” patients were given [1/Propensity score] as weight and “unmatched” patients were given [1/(1-Propensity score)] as weight.
P-values were two-sided and considered significant if ≤0.05. Statistical analysis were performed by author MS with SPSS version 22.0.
Supplemental Table 1. Patient characteristics
Characteristics / Total patients, N=347Age at diagnosis (years)
(Median, CI 95%) / 54 (52-55)
Gender
Women
Men / 204 (59%)
143 (41%)
Race
Caucasian
Other
Asian
African American
Unknown
Hispanic / 247 (71.2%)
46 (13.3%)
26 (7.5%)
13 (3.7%)
9 (2.6%)
6 (1.7%)
Type of cancer
Gastro-intestinal
Breast
Brain
Genitourinary
Head and neck
Lung
Melanoma
Othera / 94 (27.1%)
82 (23.7%)
36 (10.4%)
34 (9.8%)
33 (9.5%)
28 (8%)
26 (7.5%)
14 (4%)
aEwing sarcoma, carcinoid tumor, sarcomatoid tumor, peripheral nerve sheath tumor, pleiomorphic sarcoma (n=2), soft tissue rhabdomyosarcoma, leiomyosarcoma, and unknown origin (n=6).
1
Supplemental Table 2. List of alterations and matched drugs
# / Tumor type / Alterations / Matched drug(alteration(s) targeted bolded) / SD ≥ 6 months/PR/CR
1 / Lung / PTEN splice site 493-1 G>A / everolimus / YES
2 / Breast / EGFR amplification, CCND1 amplification, CDKN2A/B loss, FGFR1 amplification, MYC amplification, TP53 P151A / lapatinib / YES
3 / Breast / ESR1a Y537S / tamoxifen / YES
4 / Head and neck / PTEN I67K, CDKN2A/B loss, CTNNB1 T257I, MCL1 amplification / everolimus / NO
5 / Head and neck / ERBB2 amplification, FGFR4 amplification, NF1 loss, PIK3CA E545K, CCNE1 amplification, MYC amplification, TP53 D228fs*1 / everolimus + lapatinib / NO
6 / GI / FGFR2 amplification, CDKN2A loss, MYC amplification, APC I1307K, ARID1A P2139fs*62, TP53 F113C / ponatinib / NO
7 / GI / APC S1421fs*52, APC A571fs*18, TP53 Y163C / bevacizumab / NO
8 / Brain / EGFR amplification, CDKN2A loss / lapatinib / NO
9 / Breast / ERBB2 amplification / trastuzumab + lapatinib / NO
10 / Breast / RET C634R, GATA3 P436fs*11+ / sorafenib / NO
11 / Breast / AKT3 amplification, MYC amplification, MYCL1 amplification, TP53 R248Q / everolimus / NO
12 / Breast / ERBB2 amplification, MYC amplification, CDK6 amplification, TP53 R213* / trastuzumab / YES
13 / GI / KRAS G13D, MYCL1 amplification, ATM R337C, DNMT3A R882H, TP53 G266R / bevacizumab / YES
14 / Skin/Melanoma / BRAF V600E, MYC amplification, MSH6 R1068 / dabrafenib / NO
15 / Breast / ERBB2 amplification, PIK3CA H1047L, AURKA amplification, TP53 R342P, CREBBP P858S, ZNF217 amplification / trastuzumab + lapatinib + everolimus / YES
16 / Breast / MCL1 amplification, ESR1a D538G / letrozole / NO
17 / GU / FBXW7 E113D, MCL1 amplification, TP53 S241F / bevacizumab / YES
18 / GI / AURKA amplification, CCND2 amplification, KRAS G12V, MYC A59V, RICTOR amplification, TP53 R248Q, FGF23 amplification, ZNF217 amplification / bevacizumab / YES
19 / Breast / ERBB2 V777L, ERBB2 S1050*, FGFR1 amplification, PIK3CA E545K, TET2 S714*, TP53 W53*, ZNF703 amplification / trastuzumab + everolimus / YES
20 / GU / NF1 Q1315*, NF1 Q2528fs*20, PTEN G44D, BRCA2 W993*, MLL R2204Q, TP53 G244S, TP53 S215G, KDM5C splice, MLH1 splice site 1558+1G>A, PBRM1 splice, SPEN splice / everolimus / NO
21 / Breast / STK11 loss, TP53 R248W, MYC amplification, SMAD4 D415fs*14, GATA3 splice, MYST3 amplification / dasatinib / NO
22 / Breast / PTEN N329fs*3, TP53 splice site 994-2A>G / everolimus / NO
23 / Breast / PIK3CA E545K, PTEN loss, MYC amplification, TP53 truncation intron 6, DNMT3A R882C, ASXL1 G181R, MAP3K1 C667fs*4, MAP3K1 H1058fs*24 / everolimus / NO
24 / Breast / PIK3CA H1047R, CCND1 amplification, ESR1a Y537S, KDM5C S717L, FGF19 amplification, FGF3 amplification, FGF4 amplification / everolimus + exemestane / NO