TP53 tumor-suppressor gene mutations are among the most frequent abnormalities in cancer, affecting approximately 40% of patients. Yet, there is no accepted way to target these alterations in the clinic. At the same time, antagonists of VEGFR or its ligand are best-selling oncology drugs, with multiple, expensive compounds approved. Although only a subset of patients benefit from these antiangiogenesis agents, no relevant biomarker has been identified. Interestingly, TP53 mutations upregulate VEGF-A and VEGFR2. We prospectively enrolled 500 patients, to be interrogated by comprehensive genomic profiling (CGP) (next-generation sequencing, 236 genes), and to be matched, whenever possible, with targeted agents. Herein, we analyze outcomes based on VEGF/VEGFR inhibitor treatment and presence of TP53 mutations. Of the 500 patients, 188 (37.6%; with ≥1 alteration) were treated; 106 (56% of 188) had tumors that harbored TP53 mutations. VEGF/VEGFR inhibitor therapy was independently associated with improvement in all outcome parameters [rate of stable disease (SD) ≥6 months/partial and complete remission (PR/CR); (31% versus 7%; TP53-mutant patients (who received no other molecular-matched agents) treated with versus without VEGF/VEGFR inhibitors), time-to-treatment failure, and overall survival (multivariate analysis: all P ≤ 0.01)] for the patients harboring TP53-mutant cancers, but improvement was not seen in any of these parameters for patients with TP53 wild-type neoplasms. We conclude that TP53 mutations predict sensitivity to VEGF/VEGFR inhibitors in the clinic. TP53 alterations may therefore be a ready biomarker for treatment with antiangiogenesis agents, a finding of seminal importance across the cancer field. Mol Cancer Ther; 15(10); 2475–85. ©2016 AACR.

Angiogenesis fuels the proliferation and spread of cancer (1). VEGF is probably the most commonly involved proangiogenic factor. FDA-approved drugs now include antibodies against VEGF, as well as numerous small-molecule tyrosine kinase inhibitors (TKI) that target a VEGF receptor (VEGFR). Indeed, bevacizumab, an antibody targeting the VEGF-A ligand, has previously been proclaimed one of the best-selling drugs in oncology. However, for most malignancies in which it is deployed, including colon, lung, and kidney cancer, as well as glioblastoma multiforme, bevacizumab increases survival by only a few weeks. Small-molecule VEGFR inhibitors produce comparable results. Indeed, the FDA revoked its approval of bevacizumab in breast cancer because of the absence of evidence demonstrating a survival advantage, despite prior activity signals (2). It seems plausible that, in relevant neoplasms, a subgroup of patients across tumor types may be responsive to bevacizumab and/or small-molecule VEGFR inhibitors; however, biomarkers to identify and select these subsets remain elusive (3).

TP53 mutations are the most common molecular alterations across cancers, with about 40% of diverse tumors harboring this type of abnormality. Yet, there is no approved therapy that targets p53 (4, 5). We previously reported results of a retrospective study of patients with diverse cancers demonstrating that bevacizumab-containing regimens predicted for longer progression-free survival (PFS) in TP53-mutant tumors (multivariate analysis; P < 0.001; PFS = 11 vs. 5.0 months; mutant vs. wild-type TP53; ref. 6). The mechanism by which this correlation might occur remains uncertain; however, there is evidence to suggest that TP53 is implicated in angiogenesis. For instance, a multiple regression analysis of the transcriptome in non–small cell lung cancer (NSCLC) revealed that high VEGF-A expression correlated independently with TP53 mutational status (7). Though early data failed to find a clear association between VEGF expression and outcome after bevacizumab administration, recent observations using improved technology indicate that circulating levels of the short isoform of VEGF-A is a strong biomarker candidate for predicting benefits (3).

Herein, we report results of a sub-analysis of a prospective study evaluating comprehensive genomic profiling (CGP) using a 236 gene next-generation sequencing (NGS) platform for matching patients (n = 188) to drugs. These data demonstrate that patients whose tumors harbored the TP53-mutant, but not TP53–wild-type, gene had improved outcomes when treated with VEGF or VEGFR antagonists. Our current observations provide important support for the concept that TP53 mutations represent a biomarker that predicts salutary effects of anti-angiogenic agents.

Patient population and study design

The parent study enrolled 500 patients with refractory or progressive cancers. Its primary objective was to analyze the outcome of patients with diverse cancers prospectively matched to targeted therapy on the basis of CGP. An analysis of these data has been published (8). The current study represents a complete sub-analysis of the clinical correlates of TP53 status in all 188 patients exhibiting at least one molecular abnormality who were treated on study (Fig. 1). The purpose of this analysis was determining whether the presence of TP53 mutations was associated with a better outcome when antiangiogenesis agents were administered. The antiangiogenesis agents administered are shown in Supplementary Table S1, and included antibodies and small-molecule TKIs, which targeted either VEGF or VEGFR (at low IC50s). Supplementary multivariate analyses are shown in Supplementary Table S2. The study included 17 tumor types (Supplementary Table S3).

Figure 1.

Consolidated Standards of Reporting Trials (CONSORT) diagram demonstrates the flow of 500 patients who consented for the study.

Figure 1.

Consolidated Standards of Reporting Trials (CONSORT) diagram demonstrates the flow of 500 patients who consented for the study.

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Study eligibility criteria included ECOG performance status ≤1 (9), tumor (archived or fresh) likely available for CGP, a life expectancy of ≥3 months, and likely able to qualify for therapy. The study was designed as a navigation trial, where the physician could use the CGP diagnostic, in the context of the patient's other medical conditions and laboratory tests, to choose a therapy, such as an approved medication used on- or off-label or a clinical trial. This protocol (NCT02437617) was approved by the MD Anderson Internal Review Board, and all patients gave informed consent.

CGP using a broad, hybrid capture–based NGS assay

Base substitutions, insertions, deletions, copy-number alterations, and selected gene fusions were detected [Foundation Medicine (Cambridge, MA; clinical laboratory improvements amendment (CLIA) laboratory; ref. 10]. DNA was isolated from 40 μm of formalin-fixed paraffin-embedded tissue (for specimens with ≥20% tumor cells) using the Maxwell 16 FFPE Plus LEV DNA Purification kit (Promega) and quantified using a standardized PicoGreen fluorescence assay (Invitrogen). Library construction was performed with the use of a 50 to 200 ng of DNA sheared by sonication to about 100 to 400 bp before end-repair, dA addition and ligation of indexed, Illumina sequencing adaptors. Enrichment of target sequences (all coding exons of 236 cancer-related genes (3,769 exons) plus 47 introns from 19 genes often rearranged or altered in malignancy (Supplementary Table S4) was achieved by solution-based hybrid capture with custom biotinylated oligonucleotide bases. Enriched libraries were sequenced to an average median depth of >500X with 99% of bases covered >100X (IlluminaHiSeq 2000 platform using 49 × 49 paired-end reads) and mapped to the reference human genome (hg19) using the Burrows–Wheeler Aligner and the publicly available SAM tools, Picard, and Genome Analysis Toolkit. Point mutations were identified by a Bayesian algorithm; short insertions and deletions, determined by local assembly; gene copy-number alterations (amplifications), by comparison with process matched normal controls; and gene fusions/rearrangements, by clustering chimeric reads mapped to targeted introns.

Amplifications were called for ≥6 copies (>7 for triploid, >8 for tetraploid samples) except for ErbB2 (≥5 copies). Aberrations, mutations or other alterations in kinases that were presumed to be non-functional based on wet lab experiments or structural modeling, or lack of data were not included.

Antiangiogenic therapies

Antiangiogenic treatments included antibodies and small-molecule kinase inhibitors (Supplementary Table S1). These treatments could be given as single agents or in combination with other drugs. Antibodies are usually specific for angiogenic molecules, whereas small-molecule inhibitors often had targets in addition to VEGFRs.

Outcome assessment

Patients who were lost to follow-up or came off early (before restaging) for payor reasons were considered inevaluable for stable disease (SD) ≥6 months/partial and complete remission (PR/CR); patients who came off early for side effects were considered progressors. Patients who had not been restaged at the date of data cutoff (6 months after the date of the last enrollment; August 4, 2014) were considered inevaluable for SD ≥6 months/PR/CR (unless they had already achieved a CR or PR). SD, PR, or CR was determined per the assessment by the treating doctor. Time-to-treatment failure (TTF) and overall survival (OS) were assessed by the method of Kaplan and Meier, and the survival function between groups was compared using a two-sided log-rank test. TTF was the interval from the treatment start date to documentation of disease progression, death, or removal from study for any reason (mostly side effects), whichever occurred first. Patients who had not reached any of those endpoints at the date of data cutoff were censored on that date. If they had not reached the endpoints at the time of being lost to follow-up, they were censored at the date of last contact. OS was measured from the time of study enrollment. Patients still alive (for OS) at the date of data cut off, or the date of last contact for patients lost to follow-up, were censored on that date.

The treatment assessed was the first trial/therapy after receiving the CGP result. Local therapy (hepatic arterial infusion, for example) and transplant was considered inevaluable. If the therapy immediately before the on-study treatment was immunotherapy, that patient was inevaluable for all outcome parameters (SD ≥6 moths/PR/CR, TTF, OS) because of the potential for delayed responses.

Statistical analysis

Statistical analysis was verified by our statistician J.J. Lee. Patient characteristics were summarized using descriptive statistics. The χ2 test or Fisher's exact test, as appropriate was used to evaluate the associations between categorical variables and SD ≥6 months/PR/CR. The Mann–Whitney U test was applied to compare number of molecular alterations per person between groups (11).

The main endpoints of the study were SD ≥6 months/PR/CR as well as TTF, and OS. Kaplan–Meier estimates were computed for the time-to-event data (12). Log-rank test was applied for comparing the survival curves between groups. The multivariable Cox proportional hazards regression model (13) was used to examine risk factors related to TTF and OS, after adjusting for other factors, in particular TP53 mutation status. Covariates included in the stepwise multivariate analysis were those with a P value of <0.2 in univariate analysis. Only variables with P ≤ 0.10 in the multivariate analysis (and treatment with VEGF/VEGFR inhibitors) were included in the final model. Bootstrapping was performed using random sampling with replacement. The bootstrap method may be superior to approaches relying on the asymptotic distribution of the tests that assume the data come from a normal distribution.

All statistical tests were two-sided, and P ≤ 0.05 was considered statistically significant. Statistical analyses were carried out using SPSS (version 22.0; SPSS).

Patient characteristics

Of the 500 individuals enrolled, 188 patients (37.6%) with at least one molecular alteration were treated (Fig. 1). One hundred and eighty-two treated patients (97%) were evaluable for assessment of SD ≥6 months/PR/CR, and all 188 were evaluable for TTF and OS. Of patients who were not treated or not evaluable for treatment, the most frequent reasons were insufficient tissue, progressive cancer or succumbing to disease. One hundred and six patients (56% of 188) had tumors that harbored TP53 mutations.

Of 188 treated patients, 55% were 60 years old or younger, and 65% were women. Gastrointestinal malignancies were present in 18% of patients (Table 1). The median number of molecular alterations was five per person (range, 1–14). Patients were heavily pretreated. The median number of prior therapies in the metastatic setting was 4 (range, 0–17). The median OS for all 188 patients was 8.0 months (range, 0.3–23.6 months). There was a trend for participants with TP53 wild-type tumors to survive longer than the TP53-mutant tumor-bearing patients, but this did not reach statistical significance (9.2 vs. 7.6 months; P = 0.132)

Table 1.

Patient characteristics by TP53 mutation status (n = 188)

All patientsTP53 mutantTP53 wild-type
VariableGroupn = 188 (%)n = 106 (%)n = 82 (%)Pa
Age ≤60 years 103 (55) 57 (54) 46 (56) 0.865 
 >60 years 85 (45) 49 (46) 36 (44)  
Sex Women 122 (65) 73 (69) 49 (60) 0.253 
 Men 66 (35) 33 (31) 33 (40)  
Tumor type Other 154 (82) 87 (82) 67 (82) 1.000 
 Gastrointestinal 34 (18) 19 (18) 15 (18)  
Metastatic sites ≤2 103 (55) 62 (58) 41 (50) 0.311 
 >2 85 (45) 44 (42) 41 (50)  
No. of prior therapies (before therapy on study) <3 58 (31) 32 (30) 26 (32) 0.949 
 ≥3 130 (69) 74 (70) 56 (68)  
ECOG performance status 34 (20) 18 (19) 16 (21) 0.969 
(n = 170) ≥1 136 (80) 75 (81) 61 (79)  
Serum platelets ≤440 K/μL 176 (96) 100 (98) 76 (94) 0.244 
(n = 183) >440 K/μL 7 (4) 2 (2) 5 (6)  
Serum LDH ≤618 U/L 102 (67) 52 (64) 50 (69) 0.606 
(n = 153) >618 U/L 51 (33) 29 (36) 22 (31)  
Serum albumin ≥3.5 g/dL 143 (80) 82 (84) 61 (76) 0.294 
(n = 178) <3.5 g/dL 35 (20) 16 (16) 19 (24)  
PI3K pathway alteration Yes 91 (48) 54 (51) 37 (45) 0.519 
 No 97 (52) 52 (49) 45 (55)  
Anti-VEGF/VEGFR therapy Yes 61 (32) 39 (37) 22 (27) 0.197 
 No 127 (68) 67 (63) 60 (73)  
Combination Therapy Yes 123 (65) 79 (75) 44 (54) 0.005 
 No 65 (35) 27 (25) 38 (46)  
RMH scoreb ≤1 (low risk) 111 (73) 60 (74) 51 (71) 0.790 
(n = 153) >1 (high risk) 42 (27) 21 (26) 21 (29)  
MDACC scorec ≤2 (low risk) 102 (68) 54 (68) 48 (68) 1.000 
(n = 150) >2 (high risk) 48 (32) 25 (32) 23 (32)  
No. of molecular alterations per person Median <0.00001 
 Range 1–14 1–14 1–13  
No. of matches per patient (excludes VEGF/VEGFR inhibitors matched to TP53 alterations) Median 0.163 
 Range 0–9 0–6 0–9  
TTF on last prior therapy before protocol enrollment (n = 140) Median (range) months 2.6 (0.4–96.0) 2.8 (0.5–20.9) 2.5 (0.4–96.0) 0.171 
SD ≥ 6 months/PR/CR (n = 182) N (%) 28 (15) 17 (17) 11 (14) 0.786 
TTF Median (range) months 2.6 (0.1–19.0) 2.6 (0.1–15.2+)d 2.7 (0.2–19.0) 0.737 
OS Median (range) months 8.0 (0.3–23.6) 7.6 (0.4–19.0) 9.2 (0.3–23.6) 0.132 
All patientsTP53 mutantTP53 wild-type
VariableGroupn = 188 (%)n = 106 (%)n = 82 (%)Pa
Age ≤60 years 103 (55) 57 (54) 46 (56) 0.865 
 >60 years 85 (45) 49 (46) 36 (44)  
Sex Women 122 (65) 73 (69) 49 (60) 0.253 
 Men 66 (35) 33 (31) 33 (40)  
Tumor type Other 154 (82) 87 (82) 67 (82) 1.000 
 Gastrointestinal 34 (18) 19 (18) 15 (18)  
Metastatic sites ≤2 103 (55) 62 (58) 41 (50) 0.311 
 >2 85 (45) 44 (42) 41 (50)  
No. of prior therapies (before therapy on study) <3 58 (31) 32 (30) 26 (32) 0.949 
 ≥3 130 (69) 74 (70) 56 (68)  
ECOG performance status 34 (20) 18 (19) 16 (21) 0.969 
(n = 170) ≥1 136 (80) 75 (81) 61 (79)  
Serum platelets ≤440 K/μL 176 (96) 100 (98) 76 (94) 0.244 
(n = 183) >440 K/μL 7 (4) 2 (2) 5 (6)  
Serum LDH ≤618 U/L 102 (67) 52 (64) 50 (69) 0.606 
(n = 153) >618 U/L 51 (33) 29 (36) 22 (31)  
Serum albumin ≥3.5 g/dL 143 (80) 82 (84) 61 (76) 0.294 
(n = 178) <3.5 g/dL 35 (20) 16 (16) 19 (24)  
PI3K pathway alteration Yes 91 (48) 54 (51) 37 (45) 0.519 
 No 97 (52) 52 (49) 45 (55)  
Anti-VEGF/VEGFR therapy Yes 61 (32) 39 (37) 22 (27) 0.197 
 No 127 (68) 67 (63) 60 (73)  
Combination Therapy Yes 123 (65) 79 (75) 44 (54) 0.005 
 No 65 (35) 27 (25) 38 (46)  
RMH scoreb ≤1 (low risk) 111 (73) 60 (74) 51 (71) 0.790 
(n = 153) >1 (high risk) 42 (27) 21 (26) 21 (29)  
MDACC scorec ≤2 (low risk) 102 (68) 54 (68) 48 (68) 1.000 
(n = 150) >2 (high risk) 48 (32) 25 (32) 23 (32)  
No. of molecular alterations per person Median <0.00001 
 Range 1–14 1–14 1–13  
No. of matches per patient (excludes VEGF/VEGFR inhibitors matched to TP53 alterations) Median 0.163 
 Range 0–9 0–6 0–9  
TTF on last prior therapy before protocol enrollment (n = 140) Median (range) months 2.6 (0.4–96.0) 2.8 (0.5–20.9) 2.5 (0.4–96.0) 0.171 
SD ≥ 6 months/PR/CR (n = 182) N (%) 28 (15) 17 (17) 11 (14) 0.786 
TTF Median (range) months 2.6 (0.1–19.0) 2.6 (0.1–15.2+)d 2.7 (0.2–19.0) 0.737 
OS Median (range) months 8.0 (0.3–23.6) 7.6 (0.4–19.0) 9.2 (0.3–23.6) 0.132 

NOTE: Bold font indicates statistically significant, P ≤ 0.05.

Abbreviations: CR, complete response; ECOG, Eastern Cooperative Oncology Group; MDACC, MD Anderson Cancer Center; PR, partial response; RMH, Royal Marsden Hospital; TTF, time-to-treatment failure on protocol therapy.

aFisher or χ2 test as appropriate; Mann–Whitney U test for no. of molecular alterations per person, as well as number of molecular matches excluding VEGF/VEGFR inhibitors; log-rank tests for TTF and OS (Kaplan–Meier).

bThe Royal Marsden Hospital (RMH) prognostic scoring system included the following factors: LDH and albumin levels and number of sites of metastases (8, 9).

cThe MD Anderson Cancer Center (MDACC) scoring system includes the following factors: LDH and albumin levels, number of sites of metastases, ECOG performance status, and whether or not patients had gastrointestinal tumors (10).

d“+” sign indicates that the patient was still continuing on the study at the time of data cutoff.

Patient characteristics in the TP53–wild-type and -mutant groups were generally well balanced (Table 1; refs. 14–16), except that TP53 mutant versus wild-type individuals generally had a greater number of molecular alterations (median = 5 versus 3, P < 0.00001) and received combination therapies more often (P = 0.005), variables generally associated with worse and better outcomes, respectively (17, 18). Overall, TP53 mutant and wild-type groups were equivalent for both canonical composite prognostic scores–the Royal Marsden Hospital Score (RMH) score [based on levels of albumin, and lactate dehydrogenase (LDH), and number of metastatic sites] and the MD Anderson Cancer Center (MDACC) score (based on RMH with tumor type and ECOG performance status as additional variables; Table 1; refs. 14–16).

Univariate analysis of factors associated with outcome

TP53-mutant tumors (n = 106).

The only factor associated with a higher rate of SD ≥6 months/PR/CR was treatment with VEGF/VEGFR inhibitors (P = 0.020; Supplementary Table S1 for drugs used; Table 2 for univariate analysis).

Table 2.

Univariate analysis of factors affecting response, TTF, and, OS for mutant versus wild-type TP53

Patients with TP53 molecular alterations (n = 106)
VariableGroupEvaluable for SD ≥6 months/PR/CR (n = 103)aSD ≥6 months/PR/CR n (%)aPEvaluable for TTF and OS (n = 106)Median TTF (95% CI; months)PMedian OS (95% CI; months)P
Age (y) ≤60 56 9 (16) 1.000 57 1.9 (1.2–2.6) 0.652 6.9 (4.6–9.2) 0.646 
 >60 47 8 (17)  49 2.8 (1.4–4.2)  8.1 (7.1–9.1)  
Sex Women 72 12 (17) 1.000 73 2.2 (1.6–2.8) 0.545 8.1 (6.1–10.1) 0.051 
 Men 31 5 (16)  33 2.7 (1.3–4.1)  4.6 (3.3–5.9)  
Tumor type Other 85 12 (14) 0.171 87 2.6 (1.9–3.3) 0.559 7.7 (5.7–9.7) 0.665 
 Gastrointestinal 18 5 (28)  19 2.7 (0.9–4.5)  6.9 (0.0–14.5)  
Metastatic sites ≤2 59 12 (20) 0.344 62 2.7 (1.6–3.8) 0.195 9.8 (6.7–12.9) 0.015 
 >2 44 5 (11)  44 2.1 (1.4–2.8)  4.4 (3.6–5.2)  
Prior therapies <3 30 8 (27) 0.087 32 2.9 (1.4–4.4) 0.160 11.4 (7.8–15.0) 0.206 
 ≥3 73 9 (12)  74 2.1 (1.4–2.8)  6.9 (4.4–9.4)  
ECOG PSb 18 4 (22) 0.513 18 1.9 (0.2–3.6) 0.669 12.3 (5.1–19.5) 0.051 
 ≥1 73 12 (16)  75 2.7 (1.9–3.5)  6.1 (3.6–8.6)  
Plateletsb ≤440 K/μL 97 17 (18) 1.000 100 2.6 (1.8–3.4) 0.174 7.6 (5.5–9.7) 0.109 
 >440 K/μL 0 (0)  0.7 (-)  2.6 (-)  
Serum LDHb ≤618 U/L 50 10 (20) 0.696 52 3.4 (2.4–4.4) 0.001 9.6 (7.5–11.7) 0.001 
 >618 U/L 29 4 (14)  29 1.4 (0.9–1.9)  4.0 (2.2–5.8)  
Serum albuminb ≥3.5 g/dL 79 16 (20) 0.065 82 2.6 (1.6–3.6) 0.016 8.1 (6.7–9.5) 0.001 
 <3.5 g/dL 16 0 (0)  16 1.6 (0.8–2.4)  3.1 (1.4–4.9)  
PI3K pathway alteration Yes 52 5 (10) 0.102 54 1.9 (1.6–2.2) 0.090 4.5 (3.8–5.2) 0.003 
 No 51 12 (24)  52 3.3 (2.5–4.1)  9.6 (5.8–13.4)  
RMH scoreb ≤1 (low risk) 58 11 (19) 0.749 60 2.8 (2.0–3.6) 0.073 8.3 (5.6–11.0) 0.033 
 >1 (high risk) 21 3 (14)  21 1.6 (0.8–2.4)  4.0 (2.2–5.8)  
MDACC scoreb ≤2 (low risk) 52 10 (19) 0.529 54 3.2 (2.3–4.1) 0.011 8.7 (6.6–10.8) 0.003 
 >2 (high risk) 25 3 (12)  25 1.6 (1.0–2.2)  3.4 (1.2–5.6)  
Molecular alterations (N)c ≤5 58 10 (17) 1.000 59 2.8 (1.7–3.9) 0.815 8.7 (5.0–12.4) 0.289 
 >5 45 7 (16)  47 2.2 (1.4–3.0)  7.5 (4.4–10.6)  
Combination therapy Yes 76 15 (20)  79 2.8 (1.6–4.0)  7.7 (5.3–10.1)  
 No 27 2 (7) 0.226 27 1.9 (1.1–2.7) 0.257 7.5 (3.6–11.4) 0.660 
Treatment: VEGF/VEGFR inhibitors Yes 38 11 (29) 0.020 39 3.2 (1.8–4.6) 0.021 8.0 (4.1–11.9) 0.163 
 No 65 6 (9)  67 2.1 (1.4–2.8)  7.5 (4.2–10.8)  
Patients with TP53 wild-type (n = 82) 
Age (y) ≤60 44 8 (18) 0.329 46 3.3 (1.7–4.9) 0.371 11.3 (1.1–21.5) 0.020 
 >60 35 3 (9)  36 2.7 (2.1–3.3)  5.1 (2.1–8.1)  
Sex Women 47 5 (11) 0.338 49 2.7 (2.1–3.3) 0.681 10.2 (5.4–15.0) 0.853 
 Men 32 6 (19)  33 2.8 (1.4–4.2)  9.2 (5.4–13.0)  
Tumor type Other 65 9 (14) 1.000 67 2.5 (1.9–3.1) 0.998 9.9 (7.1–12.7) 0.263 
 Gastrointestinal 14 2 (14)  15 3.3 (1.2–5.4)  5.1 (3.4–6.8)  
Metastatic sites ≤2 39 7 (18) 0.487 41 3.1 (2.3–3.9) 0.151 11.1 (8.0–14.2) 0.193 
 >2 40 4 (10)  41 2.1 (1.6–2.6)  6.2 (3.3–9.1)  
Prior therapies <3 25 6 (24) 0.093 26 3.3 (1.4–5.2) 0.173 19.1 (6.2–32.0) 0.031 
 ≥3 54 5 (9)  56 2.5 (1.9–3.1)  7.2 (3.7–10.7)  
ECOG PSb 16 5 (31) 0.052 16 4.0 (3.2–4.8) 0.012 19.1 (11.3–26.9) 0.004 
 ≥1 58 6 (10)  61 2.1 (1.6–2.6)  7.1 (4.4–9.8)  
Plateletsb ≤440 K/μL 73 10 (14) 0.543 76 2.7 (1.7–3.7) 0.028 9.9 (6.3–13.5) 0.170 
 >440 K/μL 1 (20)  1.8 (0.0–4.6)  5.9 (4.0–7.8)  
Serum LDHb ≤618 U/L 49 9 (18) 0.490 50 3.1 (2.2–4.0) 0.183 14.0 (6.0–22.0) 0.002 
 >618 U/L 20 2 (10)  22 1.9 (1.6–2.2)  4.7 (3.1–6.3)  
Serum albuminb ≥3.5 g/dL 59 8 (14) 0.712 61 3.1 (2.0–4.2) 0.297 11.3 (7.0–15.6) 0.00003 
 <3.5 g/dL 18 3 (17)  19 2.1 (1.4–2.8)  4.1 (2.5–5.7)  
PI3K pathway alteration Yes 36 6 (17) 0.750 37 2.7 (2.0–3.4) 0.281 9.2 (3.6–14.8) 0.461 
 No 43 5 (12)  45 2.5 (1.5–3.5)  9.9 (5.6–14.2)  
RMH scoreb ≤1 (low risk) 50 8 (16) 1.000 51 3.4 (2.8–4.0) 0.336 14.0 (6.7–21.3) 0.002 
 >1 (high risk) 19 3 (16)  21 1.9 (1.4–2.4)  5.1 (2.7–7.5)  
MDACC scoreb ≤2 (low risk) 47 8 (17) 1.000 48 3.7 (2.9–4.5) 0.177 14.0 (6.2–21.8) 0.002 
 >2 (high risk) 21 3 (14)  23 1.9 (1.4–2.4)  4.1 (2.2–6.0)  
Molecular alterations (N)c ≤3 39 7 (18) 0.487 42 2.8 (1.8–3.8) 0.161 11.1 (8.0–14.2) 0.157 
 >3 40 4 (10)  40 2.5 (1.3–3.7)  7.2 (4.8–9.6)  
Combination therapy Yes 43 5 (12) 0.750 44 2.1 (1.2–3.0) 0.593 8.2 (5.2–11.2) 0.733 
 No 36 6 (17)  38 2.8 (1.9–3.7)  10.2 (5.9–14.5)  
Treatment: VEGF/VEGFR inhibitors Yes 20 4 (20) 0.456 22 2.3 (1.5–3.1) 0.804 8.2 (4.6–11.8) 0.818 
 No 59 7 (12)  60 2.8 (1.9–3.7)  10.2 (6.3–14.1)  
Patients with TP53 molecular alterations (n = 106)
VariableGroupEvaluable for SD ≥6 months/PR/CR (n = 103)aSD ≥6 months/PR/CR n (%)aPEvaluable for TTF and OS (n = 106)Median TTF (95% CI; months)PMedian OS (95% CI; months)P
Age (y) ≤60 56 9 (16) 1.000 57 1.9 (1.2–2.6) 0.652 6.9 (4.6–9.2) 0.646 
 >60 47 8 (17)  49 2.8 (1.4–4.2)  8.1 (7.1–9.1)  
Sex Women 72 12 (17) 1.000 73 2.2 (1.6–2.8) 0.545 8.1 (6.1–10.1) 0.051 
 Men 31 5 (16)  33 2.7 (1.3–4.1)  4.6 (3.3–5.9)  
Tumor type Other 85 12 (14) 0.171 87 2.6 (1.9–3.3) 0.559 7.7 (5.7–9.7) 0.665 
 Gastrointestinal 18 5 (28)  19 2.7 (0.9–4.5)  6.9 (0.0–14.5)  
Metastatic sites ≤2 59 12 (20) 0.344 62 2.7 (1.6–3.8) 0.195 9.8 (6.7–12.9) 0.015 
 >2 44 5 (11)  44 2.1 (1.4–2.8)  4.4 (3.6–5.2)  
Prior therapies <3 30 8 (27) 0.087 32 2.9 (1.4–4.4) 0.160 11.4 (7.8–15.0) 0.206 
 ≥3 73 9 (12)  74 2.1 (1.4–2.8)  6.9 (4.4–9.4)  
ECOG PSb 18 4 (22) 0.513 18 1.9 (0.2–3.6) 0.669 12.3 (5.1–19.5) 0.051 
 ≥1 73 12 (16)  75 2.7 (1.9–3.5)  6.1 (3.6–8.6)  
Plateletsb ≤440 K/μL 97 17 (18) 1.000 100 2.6 (1.8–3.4) 0.174 7.6 (5.5–9.7) 0.109 
 >440 K/μL 0 (0)  0.7 (-)  2.6 (-)  
Serum LDHb ≤618 U/L 50 10 (20) 0.696 52 3.4 (2.4–4.4) 0.001 9.6 (7.5–11.7) 0.001 
 >618 U/L 29 4 (14)  29 1.4 (0.9–1.9)  4.0 (2.2–5.8)  
Serum albuminb ≥3.5 g/dL 79 16 (20) 0.065 82 2.6 (1.6–3.6) 0.016 8.1 (6.7–9.5) 0.001 
 <3.5 g/dL 16 0 (0)  16 1.6 (0.8–2.4)  3.1 (1.4–4.9)  
PI3K pathway alteration Yes 52 5 (10) 0.102 54 1.9 (1.6–2.2) 0.090 4.5 (3.8–5.2) 0.003 
 No 51 12 (24)  52 3.3 (2.5–4.1)  9.6 (5.8–13.4)  
RMH scoreb ≤1 (low risk) 58 11 (19) 0.749 60 2.8 (2.0–3.6) 0.073 8.3 (5.6–11.0) 0.033 
 >1 (high risk) 21 3 (14)  21 1.6 (0.8–2.4)  4.0 (2.2–5.8)  
MDACC scoreb ≤2 (low risk) 52 10 (19) 0.529 54 3.2 (2.3–4.1) 0.011 8.7 (6.6–10.8) 0.003 
 >2 (high risk) 25 3 (12)  25 1.6 (1.0–2.2)  3.4 (1.2–5.6)  
Molecular alterations (N)c ≤5 58 10 (17) 1.000 59 2.8 (1.7–3.9) 0.815 8.7 (5.0–12.4) 0.289 
 >5 45 7 (16)  47 2.2 (1.4–3.0)  7.5 (4.4–10.6)  
Combination therapy Yes 76 15 (20)  79 2.8 (1.6–4.0)  7.7 (5.3–10.1)  
 No 27 2 (7) 0.226 27 1.9 (1.1–2.7) 0.257 7.5 (3.6–11.4) 0.660 
Treatment: VEGF/VEGFR inhibitors Yes 38 11 (29) 0.020 39 3.2 (1.8–4.6) 0.021 8.0 (4.1–11.9) 0.163 
 No 65 6 (9)  67 2.1 (1.4–2.8)  7.5 (4.2–10.8)  
Patients with TP53 wild-type (n = 82) 
Age (y) ≤60 44 8 (18) 0.329 46 3.3 (1.7–4.9) 0.371 11.3 (1.1–21.5) 0.020 
 >60 35 3 (9)  36 2.7 (2.1–3.3)  5.1 (2.1–8.1)  
Sex Women 47 5 (11) 0.338 49 2.7 (2.1–3.3) 0.681 10.2 (5.4–15.0) 0.853 
 Men 32 6 (19)  33 2.8 (1.4–4.2)  9.2 (5.4–13.0)  
Tumor type Other 65 9 (14) 1.000 67 2.5 (1.9–3.1) 0.998 9.9 (7.1–12.7) 0.263 
 Gastrointestinal 14 2 (14)  15 3.3 (1.2–5.4)  5.1 (3.4–6.8)  
Metastatic sites ≤2 39 7 (18) 0.487 41 3.1 (2.3–3.9) 0.151 11.1 (8.0–14.2) 0.193 
 >2 40 4 (10)  41 2.1 (1.6–2.6)  6.2 (3.3–9.1)  
Prior therapies <3 25 6 (24) 0.093 26 3.3 (1.4–5.2) 0.173 19.1 (6.2–32.0) 0.031 
 ≥3 54 5 (9)  56 2.5 (1.9–3.1)  7.2 (3.7–10.7)  
ECOG PSb 16 5 (31) 0.052 16 4.0 (3.2–4.8) 0.012 19.1 (11.3–26.9) 0.004 
 ≥1 58 6 (10)  61 2.1 (1.6–2.6)  7.1 (4.4–9.8)  
Plateletsb ≤440 K/μL 73 10 (14) 0.543 76 2.7 (1.7–3.7) 0.028 9.9 (6.3–13.5) 0.170 
 >440 K/μL 1 (20)  1.8 (0.0–4.6)  5.9 (4.0–7.8)  
Serum LDHb ≤618 U/L 49 9 (18) 0.490 50 3.1 (2.2–4.0) 0.183 14.0 (6.0–22.0) 0.002 
 >618 U/L 20 2 (10)  22 1.9 (1.6–2.2)  4.7 (3.1–6.3)  
Serum albuminb ≥3.5 g/dL 59 8 (14) 0.712 61 3.1 (2.0–4.2) 0.297 11.3 (7.0–15.6) 0.00003 
 <3.5 g/dL 18 3 (17)  19 2.1 (1.4–2.8)  4.1 (2.5–5.7)  
PI3K pathway alteration Yes 36 6 (17) 0.750 37 2.7 (2.0–3.4) 0.281 9.2 (3.6–14.8) 0.461 
 No 43 5 (12)  45 2.5 (1.5–3.5)  9.9 (5.6–14.2)  
RMH scoreb ≤1 (low risk) 50 8 (16) 1.000 51 3.4 (2.8–4.0) 0.336 14.0 (6.7–21.3) 0.002 
 >1 (high risk) 19 3 (16)  21 1.9 (1.4–2.4)  5.1 (2.7–7.5)  
MDACC scoreb ≤2 (low risk) 47 8 (17) 1.000 48 3.7 (2.9–4.5) 0.177 14.0 (6.2–21.8) 0.002 
 >2 (high risk) 21 3 (14)  23 1.9 (1.4–2.4)  4.1 (2.2–6.0)  
Molecular alterations (N)c ≤3 39 7 (18) 0.487 42 2.8 (1.8–3.8) 0.161 11.1 (8.0–14.2) 0.157 
 >3 40 4 (10)  40 2.5 (1.3–3.7)  7.2 (4.8–9.6)  
Combination therapy Yes 43 5 (12) 0.750 44 2.1 (1.2–3.0) 0.593 8.2 (5.2–11.2) 0.733 
 No 36 6 (17)  38 2.8 (1.9–3.7)  10.2 (5.9–14.5)  
Treatment: VEGF/VEGFR inhibitors Yes 20 4 (20) 0.456 22 2.3 (1.5–3.1) 0.804 8.2 (4.6–11.8) 0.818 
 No 59 7 (12)  60 2.8 (1.9–3.7)  10.2 (6.3–14.1)  

NOTE: Bold font indicates statistically significant, P ≤ 0.05.

Abbreviations: CR, complete response; CI, confidence interval; ECOG, Eastern Cooperative Oncology Group; MDACC, MD Anderson Cancer Center; PR, partial response; PS, performance status; RMH, Royal Marsden Hospital; TTF, time to treatment failure on protocol therapy; VEGF, vascular endothelial growth factor.

an equals total number of patients available for outcome analysis; the numbers of patients available for assessment of each of SD ≥6 months/PR/CR, TTF or OS may differ slightly.

bNumbers of patients with available data for different outcome assessments may differ slightly.

cDichotomized at median.

Lower LDH, higher albumin, lower MDACC prognostic score and treatment with VEGF/VEGFR targeting agents were associated with longer TTF (all P ≤ 0.05).

Lower number of metastatic sites, low LDH, higher albumin, absence of PI3K pathway aberrations, and lower RMH or MDACC prognostic score were associated with better OS. Treatment with VEGF/VEGFR targeting agents showed a trend toward association with longer survival (P = 0.163).

TP53–wild-type tumors (n = 82).

No factors were associated with a higher rate of SD ≥6 months/PR/CR. In contrast with the TP53-mutant patients, in whom the VEGF/VEGFR inhibitors were significantly associated with a higher rate of SD ≥6 months/PR/CR (P = 0.02), for the TP53 wild-type tumors, the p value was 0.456 (Table 2).

Only better ECOG performance status and lower platelet counts correlated with better TTF. In contrast with the TP53-mutant patients, where VEGF/VEGFR inhibitors were significantly associated with longer TTF (P = 0.021), for the TP53 wild-type tumors, the P value was 0.804 (and median TTF was actually numerically shorter for the VEGF/VEGFR inhibitor-exposed patients).

For OS, younger age, better ECOG performance status, few number of prior therapies, lower LDH, higher albumin and better RMH or MDACC prognostic scores were correlated with a better outcome (all P ≤ 0.05). In contrast with the TP53-mutant patients, where VEGF/VEGFR inhibitors were associated with a trend toward a longer OS (P = 0.163), for the TP53 wild-type tumors, the P value was 0.818 (and median OS was actually numerically shorter for the VEGF/VEGFR inhibitor-exposed patients).

Additional alterations in VEGFR/VEGF family genes

Only two patients with a TP53 mutation also carried an additional alteration in a VEGF/VEGFR-related gene. These patients both had a KDR (VEGFR2) amplification. One achieved SD ≥6 months/PR/CR and the other did not; therefore, the improved response rate for VEGF/VEGFR inhibitors in TP53-mutant patients could not be attributed to the presence of another VEGF/VEGFR-related alteration.

Multivariate analysis of factors associated with outcome

The multivariate analysis was performed first by including all variables with a p value of <0.2 in the univariate analyses, and then selecting the variables with a P value of <0.1 and repeating the analysis (Supplementary Table S2). Using this methodology, treatment with VEGF/VEGFR inhibitors was independently associated with improvement in all outcome parameters [rate of SD ≥6 months/PR/CR, length of TTF and OS (all P ≤ 0.01)] for the patients harboring TP53-mutant cancers, but improvement was not seen in any of these parameters for the group of patients with TP53 wild-type neoplasms (Fig. 2A and B, TTF for TP53-mutant versus wild type). (In general, the number of patients with each tumor type was too small to sub-analyze. We did examine gastrointestinal versus other tumors. The rate of SD ≥ 6 months/PR/CR for TP53-mutant gastrointestinal tumors treated with VEGF/VEGFR inhibitors was 29% (2/7 patients) versus 29% (9/31) for p53-mutant other tumors (P = 1.000).

Figure 2.

Kaplan–Meier curves of 188 patients for TTF by type of therapy (anti-VEGF/VEGFR therapy vs. no anti-VEGF/VEGFR therapy). A, TTF of TP53-mutant patients (n = 106). B, TTF of TP53 wild-type patients (n = 82). Survival differences were analyzed by the Kaplan–Meier method (log rank test). Overall, patients with TP53-mutant cancers (A), but not those with wild-type tumors (B), demonstrated improved TTF on VEGF/VEGFR inhibitors.

Figure 2.

Kaplan–Meier curves of 188 patients for TTF by type of therapy (anti-VEGF/VEGFR therapy vs. no anti-VEGF/VEGFR therapy). A, TTF of TP53-mutant patients (n = 106). B, TTF of TP53 wild-type patients (n = 82). Survival differences were analyzed by the Kaplan–Meier method (log rank test). Overall, patients with TP53-mutant cancers (A), but not those with wild-type tumors (B), demonstrated improved TTF on VEGF/VEGFR inhibitors.

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A second analysis was performed similarly, except that the variable of interest—treatment with VEGF/VEGFR inhibitors—was included in the analysis; even if the P value for this variable was greater than 0.2 in the univariate analysis. Under these conditions, treatment with VEGF/VEGFR inhibitors was again independently associated with improvement in all outcome parameters (SD ≥6 months/PR/CR, TTF and OS) in the TP53-mutant group (all P values ≤0.012)] (Table 3). For the TP53 wild-type group, when VEGF/VEGFR treatment was forced into the multivariate analysis despite its insignificance in univariate analysis, it was associated with an improved rate of SD ≥6 months/PR/CR (univariate P value = 0.456; multivariate P value = 0.038; bootstrapping method) but the impact on TTF and OS remained insignificant. Patients often received combination regimens and may have been given matched therapy (concomitant with VEGF/VEGFR inhibitors); indeed, 65% of the VEGF/VEGFR-treated wild-type patients received another match whereas only 38% of the TP53-mutant patients treated with VEGF/VEGFR inhibitors had other matches (P = 0.0039). We therefore analyzed the subgroup of patients whose regimens included no matches to their genomic anomalies (other than VEGF/VEGFR matched to TP53 alterations; Table 4). Comparing patients who had VEGF/VEGFR inhibitors as part of their regimens versus those who did not, in individuals with TP53 mutations and no other matches, the rate of SD ≥6 months/PR/CR was 31% versus 7% (P = 0.04); in contrast, for the patients whose tumors harbored wild-type TP53, the P value was 0.49, indicating no difference in response rate between VEGF/VEGFR inhibitor treatment or not (in the absence of other matches). Similarly, in the absence of other matches, TTF was longer for the TP53-mutant individuals who were treated with VEGF/VEGFR inhibitors versus those who were not [P = 0.005; TP53-mutant patients (VEGF/VEGFR inhibitors yes vs. no)], but not for TP53 wild-type patients (P = 0.543; TP53 wild-type patients treated with VEGF/VEGFR inhibitors, yes versus no; with the TP53 wild-type patients actually showing numerically shorter TTF in the VEGF/VEGFR–treated patients). Similarly, in the absence of other matches, OS showed a trend toward improvement in the TP53-mutant patients who received VEGF/VEGFR inhibitors versus those who did not [9.6 vs. 5.2 months (P = 0.07)], whereas this was not seen in the patients whose tumors were wild-type TP53 (P = 0.607). These data confirm that, in the absence of other concomitant matched agents, VEGF/VEGFR inhibitors are associated with improvement in all outcome parameters in TP53-mutant, but not in TP53 wild-type, malignancies.

Table 3.

Multivariate analysis of factors affecting treatment outcomes in 106 patients with TP53 molecular alterations versus 82 patients with TP53 wild-type, by individual factors of MDACC score versus MDACC score (anti-VEGF/VEGFR included in multivariate regardless of univariate P)

Treatment outcomes in 106 TP53-mutant patients
SD ≥6 months/PR/CRIndividual factorsMDACC score
Asymptotic designBootstrappingdAsymptotic designBootstrapping
VariableaORb95% CIP95% CIPORb95% CIP95% CIP
Prior therapy <3 (vs. ≥3) 3.4 1.0–12.3 0.057 0.0–2.8 0.045 — — — — — 
Albumin ≥3.5 g/dL (vs. <3.5 g/dL) j   18.6–20.4 0.001 — — — — — 
Anti VEGF/VEGFR therapy Yes (vs. No) 7.1 2.0–25.6 0.003 0.2–25.8 0.002 — — — — — 
TTF Individual factors MDACC score 
  Asymptotic design Bootstrappinge  Asymptotic design Bootstrappingg 
Variablea HRc 95% CI P 95% CI P HRc 95% CI P 95% CI P 
Prior therapy ≥3 (vs. <3) 2.1 1.2–3.6 0.007 0.1–1.5 0.016      
Platelets >440 K/μL(≤440 K/μL) 3.4 0.8–14.8 0.107 0.4–2.2 0.005 5.4 1.2–24.1 0.026 0.9–2.9 0.001 
LDH >618 IU/L (vs. ≤618 IU/L) 4.3 2.4–7.6 <0.0001 0.8–2.3 0.001      
Albumin <3.5 g/dL (vs. ≥3.5 g/dL) 2.1 1.1–4.2 0.033 (−0.1)–2.1 0.060      
Anti VEGF/VEGFR therapy No (vs. Yes) 3.1 1.8–5.4 0.00005 0.4–1.9 0.002 2.5 1.4–4.5 0.001 0.3–1.6 0.001 
MDACC score >2 (vs. ≤2)      3.2 1.8–5.8 0.0001 0.5–2.0 0.001 
OS Individual factors MDACC score 
  Asymptotic design Bootstrappingf  Asymptotic design Bootstrappingh 
Variablea HRc 95% CI P 95% CI P HRc 95% CI P 95% CI P 
Men (vs. Women) 3.1 1.6–5.9 0.001 0.4–2.2 0.002 1.9 1.1–3.3 0.023 (−0.1)–1.7 0.055 
Metastatic sites >2 (vs. ≤2) 1.7 0.9–3.1 0.078 (−0.2)–1.5 0.082      
Platelets >440 K/μL(≤440 K/μL) — — — — — 3.2 0.7–13.8 0.119 0.3–3.1 0.067 
LDH >618 IU/L (vs. ≤618 IU/L) 4.9 2.4–9.9 0.000009 0.8–2.8 0.001      
Albumin <3.5 g/dL (vs. ≥3.5 g/dL) 2.8 1.3–6.0 0.009 0.0–2.7 0.029      
PI3K pathway alteration No (vs. Yes) 0.5 0.3–1.0 0.033 (−1.4 to −0.1) 0.079 0.4 0.2–0.7 0.003 (−1.8 to −0.3) 0.005 
Anti VEGF/VEGFR therapy No (vs. Yes) 2.6 1.3–5.0 0.005 0.2–1.9 0.012 2.3 1.2–4.5 0.013 0.1–1.9 0.016 
MDACC score >2 (vs. ≤2)      5.3 2.6–10.6 0.000003 0.9–2.8 0.001 
Treatment outcomes in 82 TP53 wild-type patients 
SD ≥6 months/PR/CR Individual factors MDACC score 
  Asymptotic design Bootstrappingd  Asymptotic design Bootstrapping 
Variablea ORb 95% CI P 95% CI P ORb 95% CI P 95% CI P 
Prior therapy <3 (vs. ≥3) 4.3 0.9–20.7 0.068 (−0.5)–20.7 0.035 — — — — — 
ECOG PS 0 (vs. ≥1) 4.3 1.0–19.0 0.058 (−0.8)–20.9 0.022 — — — — — 
Anti VEGF/VEGFR therapy Yes (vs. No) 4.7 0.8–25.7 0.076 (−18.4)–21.4 0.038 — — — — — 
TTF Individual factors MDACC score 
  Asymptotic design Bootstrappingf  Asymptotic design Bootstrappingi 
Variablea HRc 95% CI P 95% CI P HRc 95% CI P 95% CI P 
ECOG PS ≥1 (vs. 0) 2.5 1.3–4.9 0.007 0.2–1.9 0.009      
Platelets >440 K/μL (≤440 K/μL) 3.1 1.2–8.2 0.020 0.2–2.8 0.030 2.8 1.1–7.0 0.034 0.3–3.0 0.056 
Molecular alterations >3 (vs. ≤3) 1.8 1.1–3.0 0.024 0.0–1.1 0.023      
Anti-VEGF/VEGFR therapy No (vs. Yes) 1.6 0.9–2.8 0.30 (−0.3)–1.3 0.52 1.1 0.7–1.9 0.667 (−0.5)–0.8 0.660 
MDACC score >2 (vs. ≤2)           
OS Individual factors MDACC score 
  Asymptotic design Bootstrappingd  Asymptotic design Bootstrappingd 
Variablea HRc 95% CI P 95% CI P HRc 95% CI P 95% CI P 
LDH >618 IU/L (vs. ≤618 IU/L) 2.8 1.4–5.5 0.004 0.4–1.8 0.002      
Anti-VEGF/VEGFR therapy No (vs. Yes) 0.9 0.4–1.7 0.707 (−0.9–0.6) 0.705 0.8 0.4–1.6 0.527 (−1.0)–0.7 0.528 
MDACC score >2 (vs. ≤2)      2.7 1.4–5.3 0.003 0.2–2.0 0.005 
Treatment outcomes in 106 TP53-mutant patients
SD ≥6 months/PR/CRIndividual factorsMDACC score
Asymptotic designBootstrappingdAsymptotic designBootstrapping
VariableaORb95% CIP95% CIPORb95% CIP95% CIP
Prior therapy <3 (vs. ≥3) 3.4 1.0–12.3 0.057 0.0–2.8 0.045 — — — — — 
Albumin ≥3.5 g/dL (vs. <3.5 g/dL) j   18.6–20.4 0.001 — — — — — 
Anti VEGF/VEGFR therapy Yes (vs. No) 7.1 2.0–25.6 0.003 0.2–25.8 0.002 — — — — — 
TTF Individual factors MDACC score 
  Asymptotic design Bootstrappinge  Asymptotic design Bootstrappingg 
Variablea HRc 95% CI P 95% CI P HRc 95% CI P 95% CI P 
Prior therapy ≥3 (vs. <3) 2.1 1.2–3.6 0.007 0.1–1.5 0.016      
Platelets >440 K/μL(≤440 K/μL) 3.4 0.8–14.8 0.107 0.4–2.2 0.005 5.4 1.2–24.1 0.026 0.9–2.9 0.001 
LDH >618 IU/L (vs. ≤618 IU/L) 4.3 2.4–7.6 <0.0001 0.8–2.3 0.001      
Albumin <3.5 g/dL (vs. ≥3.5 g/dL) 2.1 1.1–4.2 0.033 (−0.1)–2.1 0.060      
Anti VEGF/VEGFR therapy No (vs. Yes) 3.1 1.8–5.4 0.00005 0.4–1.9 0.002 2.5 1.4–4.5 0.001 0.3–1.6 0.001 
MDACC score >2 (vs. ≤2)      3.2 1.8–5.8 0.0001 0.5–2.0 0.001 
OS Individual factors MDACC score 
  Asymptotic design Bootstrappingf  Asymptotic design Bootstrappingh 
Variablea HRc 95% CI P 95% CI P HRc 95% CI P 95% CI P 
Men (vs. Women) 3.1 1.6–5.9 0.001 0.4–2.2 0.002 1.9 1.1–3.3 0.023 (−0.1)–1.7 0.055 
Metastatic sites >2 (vs. ≤2) 1.7 0.9–3.1 0.078 (−0.2)–1.5 0.082      
Platelets >440 K/μL(≤440 K/μL) — — — — — 3.2 0.7–13.8 0.119 0.3–3.1 0.067 
LDH >618 IU/L (vs. ≤618 IU/L) 4.9 2.4–9.9 0.000009 0.8–2.8 0.001      
Albumin <3.5 g/dL (vs. ≥3.5 g/dL) 2.8 1.3–6.0 0.009 0.0–2.7 0.029      
PI3K pathway alteration No (vs. Yes) 0.5 0.3–1.0 0.033 (−1.4 to −0.1) 0.079 0.4 0.2–0.7 0.003 (−1.8 to −0.3) 0.005 
Anti VEGF/VEGFR therapy No (vs. Yes) 2.6 1.3–5.0 0.005 0.2–1.9 0.012 2.3 1.2–4.5 0.013 0.1–1.9 0.016 
MDACC score >2 (vs. ≤2)      5.3 2.6–10.6 0.000003 0.9–2.8 0.001 
Treatment outcomes in 82 TP53 wild-type patients 
SD ≥6 months/PR/CR Individual factors MDACC score 
  Asymptotic design Bootstrappingd  Asymptotic design Bootstrapping 
Variablea ORb 95% CI P 95% CI P ORb 95% CI P 95% CI P 
Prior therapy <3 (vs. ≥3) 4.3 0.9–20.7 0.068 (−0.5)–20.7 0.035 — — — — — 
ECOG PS 0 (vs. ≥1) 4.3 1.0–19.0 0.058 (−0.8)–20.9 0.022 — — — — — 
Anti VEGF/VEGFR therapy Yes (vs. No) 4.7 0.8–25.7 0.076 (−18.4)–21.4 0.038 — — — — — 
TTF Individual factors MDACC score 
  Asymptotic design Bootstrappingf  Asymptotic design Bootstrappingi 
Variablea HRc 95% CI P 95% CI P HRc 95% CI P 95% CI P 
ECOG PS ≥1 (vs. 0) 2.5 1.3–4.9 0.007 0.2–1.9 0.009      
Platelets >440 K/μL (≤440 K/μL) 3.1 1.2–8.2 0.020 0.2–2.8 0.030 2.8 1.1–7.0 0.034 0.3–3.0 0.056 
Molecular alterations >3 (vs. ≤3) 1.8 1.1–3.0 0.024 0.0–1.1 0.023      
Anti-VEGF/VEGFR therapy No (vs. Yes) 1.6 0.9–2.8 0.30 (−0.3)–1.3 0.52 1.1 0.7–1.9 0.667 (−0.5)–0.8 0.660 
MDACC score >2 (vs. ≤2)           
OS Individual factors MDACC score 
  Asymptotic design Bootstrappingd  Asymptotic design Bootstrappingd 
Variablea HRc 95% CI P 95% CI P HRc 95% CI P 95% CI P 
LDH >618 IU/L (vs. ≤618 IU/L) 2.8 1.4–5.5 0.004 0.4–1.8 0.002      
Anti-VEGF/VEGFR therapy No (vs. Yes) 0.9 0.4–1.7 0.707 (−0.9–0.6) 0.705 0.8 0.4–1.6 0.527 (−1.0)–0.7 0.528 
MDACC score >2 (vs. ≤2)      2.7 1.4–5.3 0.003 0.2–2.0 0.005 

NOTE: Bold font indicates statistically significant, P ≤ 0.05.

Abbreviations: CI, confidence interval; MDACC, MD Anderson Cancer Center; TTF, time to treatment failure on protocol therapy.

aMultivariate included variables with P value <0.2 in the univariate analyses and the variable “Anti VEGF/VEGFR therapy”; the analysis was then repeated with variables that had cut off of <0.1.

bOR >1 = increases odds for SD ≥6 months/PR/CR.

cHR >1 = shorter TTF/OS.

Number of samples generated by boot strapping method:

d1,000.

e866.

f994.

g868.

h864.

i992.

jOR is undefined for albumin as one of the cells had a value of “zero.”

Table 4.

Univariate analysis of factors affecting SD ≥6 months/PR/CR, TTF, and, OS in 60 TP53-mutant and 36 TP53 wild-type patients with no other matches

SD ≥6 months/PR/CR n (%)Median TTF (95% CI; months)Median OS (95% CI; months)
VariableTP53 mutantTP53 wild-typeTP53 mutantTP53 wild-typeTP53 mutantTP53 wild-type
VEGF/VEGFR inhibitors (yes) n = 29a n = 7a n = 30 n = 8 n = 30 n = 8 
 9 (31) 1 (14) 2.8 (0.7–4.9) 1.9 (1.6–2.2) 9.6 (4.2–15.0) 8.2 (6.7–9.7) 
VEGF/VEGFR inhibitors (no) n = 29a n = 28a n = 30 n = 28 n = 30 n = 28 
 2 (7) 2 (7) 1.6 (1.2–2.0) 2.1 (0.7–3.5) 5.2 (3.3–7.1) 7.2 (3.6–10.8) 
P 0.044 0.499 0.005 0.543 0.070 0.607 
SD ≥6 months/PR/CR n (%)Median TTF (95% CI; months)Median OS (95% CI; months)
VariableTP53 mutantTP53 wild-typeTP53 mutantTP53 wild-typeTP53 mutantTP53 wild-type
VEGF/VEGFR inhibitors (yes) n = 29a n = 7a n = 30 n = 8 n = 30 n = 8 
 9 (31) 1 (14) 2.8 (0.7–4.9) 1.9 (1.6–2.2) 9.6 (4.2–15.0) 8.2 (6.7–9.7) 
VEGF/VEGFR inhibitors (no) n = 29a n = 28a n = 30 n = 28 n = 30 n = 28 
 2 (7) 2 (7) 1.6 (1.2–2.0) 2.1 (0.7–3.5) 5.2 (3.3–7.1) 7.2 (3.6–10.8) 
P 0.044 0.499 0.005 0.543 0.070 0.607 

NOTE: Bold font indicates statistically significant, P ≤ 0.05.

Abbreviations: CI, confidence interval; TTF, time to treatment failure on protocol therapy.

an equals total number of patients available for outcome analysis; the numbers of patients available for assessment of each of SD ≥6 months/PR/CR, TTF, or OS may differ slightly.

We have previously described, in a retrospective study, a clinical association between TP53 mutations and better PFS after bevacizumab treatment (6). Furthermore, a comprehensive transcriptomic analysis in NSCLC demonstrated that, in multivariate analysis, TP53 mutations were an independent predictor of high expression of VEGF-A (7), the primary target of bevacizumab, and the ligand for VEGFR-1 (Flt-1) and VEGFR-2 (KDR/Flk-1). The mechanism underlying the association between TP53 mutations and angiogenesis through VEGF-A is complicated, but may be in part related to the fact that wild-type TP53 indirectly represses VEGF expression by interaction with, and inhibition of, transcription factors such as SP1 and E2F (reviewed in ref. 19). Our current study was a prospective navigation trial and provides validation for the concept that TP53 mutations are a biomarker predicting beneficial effects when VEGF/VEGFR inhibitors are used in the clinic.

In recent years, molecular interrogation of tumors and deployment of matched therapies has shown remarkable responses in a variety of refractory malignancies (4). Aberrant TP53 has, however, proven to be difficult to target, though several approaches are being attempted. For instance, Mdm2 inhibitor molecules demonstrate preclinical promise in a wide variety of tumors with wild-type TP53 (20). In addition, AZD-1775, a potent and selective small-molecule inhibitor of Wee-1 kinase (a tyrosine kinase that is involved in G2 checkpoint signaling) selectively sensitizes tumors to DNA damaging agents, probably because TP53 is a key regulator in the G1 checkpoint, and TP53-deficient tumors depend on the G2 checkpoint after DNA damage (21). More recent work has demonstrated that the p53 protein requires the binding of a single zinc ion for proper folding, and impairing zinc binding is a major mechanism for loss of function in missense mutant p53. A new class of drugs termed zinc metallochaperones may reactivate wild-type p53 structure and function by restoring zinc binding in p53-mutant tumors (22). Overall, however, even though TP53 mutations are among the most common in cancer, there is a dearth of strategies in the clinic to target this frequently altered tumor suppressor. Our current multivariate analysis demonstrates that the administration of VEGF/VEGFR inhibitors is an independent variable significantly associated with improvement in all outcome parameters in patients with TP53-mutant, but not TP53 wild-type, malignancies (Tables 2, 3, and 4, Supplementary Table S2, and Fig. 2).

Other mechanisms, in addition to upregulation of the VEGF-A ligand, may also be operative in the relationship between TP53 and angiogenesis. Indeed, the role of TP53 is broad and reaches far beyond its most commonly studied effects on apoptosis (5, 23–27). TP53 leads to activation of target genes that regulate “healthy” biological processes, including cell-cycle arrest, apoptosis, senescence, energy metabolism, and antioxidant defense to prevent tumorigenesis. In addition to losing tumor suppressive functions of wild-type TP53, proteins associated with mutant TP53 also gain new oncogenic functions, including tumor cell proliferation, antiapoptosis, angiogenesis, metastasis-promotion, and metabolic changes defined as mutant TP53 gain-of-function. A role for TP53 in immune response has also been defined, and here angiogenesis also plays a key role. Importantly, VEGF suppresses the maturation of dendritic cells from hematopoietic progenitors (28). In turn, inadequate function of dendritic cells is one mechanism of tumor escape from “normal” immune system control. Indeed, decreased dendritic cells in tissue result in a dramatically reduced ability of these cells to stimulate allogeneic T cells and antigen-specific primary T-cell response. In a preclinical model, treatment of pulsed dendritic cells in combination with an anti-VEGF antibody led to tumor shrinkage and was associated with significant anti–p53 T-cell response (29). Hence, TP53 mutations may lead to attenuation of immune responses, which can be recovered, at least in some models, with dendritic cell treatment together with antiangiogenesis agents.

Expression analyses have demonstrated that VEGFR2 upregulation may be another mechanism by which mutant p53 controls angiogenesis (30). VEGFR2 is a receptor tyrosine kinase activated by binding of the VEGF-A ligand, and it is the main receptor mediating endothelial cell neovascularization. Mutant p53 binds near the VEGFR2 promoter transcriptional initiation site and plays a role in preserving an open conformation at that location. Relatedly, mutant p53 cooperates with the SWI/SNF complex, which is essential for remodeling the VEGFR2 promoter. Given that wild-type TP53 is a canonical repressor of the VEGF pathway through multiple mechanisms, including transcriptional repression of VEGF-A (7, 19, 31, 32), loss of wild-type p53 function/presence of mutant p53 has several mechanisms by which to turn on the angiogenic “switch.” These mechanisms are compatible with increased susceptibility of TP53-mutant tumors to the effects of VEGF or VEGFR antagonists (5, 33) as confirmed in the current investigation.

There are several limitations to this study. First, there were insufficient numbers of patients to determine whether specific alterations in TP53 are more important than others. Preclinical work has also not clarified which of the TP53 mutations may function to upregulate VEGF-A or VEGFR2, or participate in the immune response. Furthermore, because this study was conducted across tumor types, it remains unclear whether or not there would be differences in significance in specific histologies. Of note, however, in this regard, our patients were well balanced in the number of gastrointestinal versus other tumors (Table 1), and the fact that the study was histology agnostic may suggest generalizability. Another limitation was that a variety of VEGF/VEGFR inhibitors were given. The strength of correlation with benefit by agent could not be analyzed based on the sample size. Still, previous data have suggested that TP53 mutations may correlate with better outcome after either VEGF antibody (6) or VEGFR small-molecule inhibitor treatment (33), and this concept is further supported by the observation that TP53 mutations are associated with increased levels of VEGF-A transcripts (7), which in turn interact with VEGFR1 and VEGFR2. Finally, our study sample size was small. Further prospective studies with larger numbers of treated patients are needed.

In summary, there are at least 12 VEGF/VEGFR inhibitors that are approved, and many more in clinical trials; none have a biomarker for patient selection (34–44). Yet, it is known that most of these agents improve survival by only a few weeks, most likely because a subset of patients respond, whereas others do not benefit or may even be harmed by these drugs. The importance of this problem is further underscored by the immense costs of antiangiogenesis agents, which often runs at thousands of dollars per month. At the same time, TP53 is a tumor-suppressor gene that affects a wealth of cancer-related pathways, and is the most common alteration across diverse tumor types. Yet, to date, this gene has not proven susceptible to targeted agents in the clinic. Our current data support the concept that TP53 status may represent a ready biomarker for response to anti-angiogenesis agents, with multivariate analysis demonstrating significant improvement in all outcome parameters when individuals with TP53-mutant, but not TP53 wild-type, advanced neoplasms were treated with anti-angiogenesis agents. Recently, TP53 mutational status was also shown to be predictive of response to the VEGFR inhibitor pazopanib in advanced sarcomas (45). The underlying mechanism of response may relate to TP53 mutations being associated with upregulation of VEGF-A and VEGFR2 expression (7, 30–32). Of interest in this regard, other tumor suppressors that are not directly druggable may also be actionable based on the upregulation of affected pathways when the suppressor gene is mutated (46). For example, PTEN alterations are not directly targeted; rather they result in upregulation of the PI3K–Akt–mTor pathway, which is actionable (47). In addition, it seems likely that the magnitude of potential benefit in patients with TP53-mutant tumors versus TP53–wild-type is increased because of a distinct role of anti-VEGF in modulating T-cell response. Treatment of TP53-mutant tumors with VEGF/VEGFR inhibitors likely increases dendritic cells, thus promoting innate T-cell response and antitumor activity (29). Overall, our observations indicate that mutant TP53 may be a pharmacologically tractable alteration that can be prosecuted by VEGF/VEGFR inhibitors. This concept requires validation by prospective randomized trials.

Y. Li ownership interest (including patents) in Foundation Medicine. A.M. Tsimberidou reports receiving a commercial research grant Foundation Medicine. V. Miller is CMO of and has ownership interest (including patents) in Foundation Medicine. R. Yelensky is VP Biomarkers and CDx and has ownership interest (including patents) in Foundation Medicine. R. Kurzrock is employed by and has ownership interest (including patents) in Novena, Inc. and Curematch, Inc.; reports receiving a commercial research grants from Genentech, Merck Serono, Pfizer, Sequenom, Foundation Medicine, and Guardant; and is a consultant/advisory board member for Sequenom, Actuate Therapeutics, and Xbioetch. No potential conflicts of interest were disclosed by the other authors.

Conception and design: J.J. Wheler, A. Naing, S. Fu, A.M. Tsimberidou, R. Yelensky, J.J. Lee, D. Hong

Development of methodology: J.J. Wheler, A.M. Tsimberidou, R. Yelensky

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J.J. Wheler, F. Janku, A. Naing, R. Zinner, V. Subbiah, S. Fu, D. Karp, G.S. Falchook, A.M. Tsimberidou, S. Piha-Paul, D. Ke, V. Miller, D. Hong

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J.J. Wheler, B. Stephen, V. Subbiah, S. Fu, R. Yelensky, J.J. Lee, D. Hong, R. Kurzrock

Writing, review, and/or revision of the manuscript: J.J. Wheler, F. Janku, A. Naing, V. Subbiah, S. Fu, D. Karp, G.S. Falchook, A.M. Tsimberidou, S. Piha-Paul, V. Miller, J.J. Lee, R. Kurzrock

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): J.J. Wheler, F. Janku, Y. Li, B. Stephen, V. Subbiah, A.M. Tsimberidou, R. Anderson

Study supervision: J.J. Wheler, D. Karp, R. Anderson, R. Kurzrock

We thank the patients and their families. The authors thank Hemwattie Ramnauth, Nilza Marie Biaggie Labiosa, and, Thomas Vu in the Department of Investigational Cancer Therapeutics at MD Anderson Cancer Center for their role in patient enrollment, regulatory work, and data collection.

This study was supported in part by a research grant from Foundation Medicine, Inc. (to J. J. Wheler).

1.
Bellou
S
,
Pentheroudakis
G
,
Murphy
C
,
Fotsis
T
. 
Anti-angiogenesis in cancer therapy: Hercules and hydra
.
Cancer Lett
2013
;
338
:
219
28
.
2.
U.S. Department of Health and Human Services USFaDA
. 
FDA Commissioner Removes Breast Cancer Indication from Avastin Label
.
Maryland
2011
.
3.
Lambrechts
D
,
Lenz
HJ
,
de Haas
S
,
Carmeliet
P
,
Scherer
SJ
. 
Markers of response for the antiangiogenic agent bevacizumab
.
J Clin Oncol
2013
;
31
:
1219
30
.
4.
Tsimberidou
AM
,
Iskander
NG
,
Hong
DS
,
Wheler
JJ
,
Falchook
GS
,
Fu
S
, et al
Personalized medicine in a phase I clinical trials program: the MD Anderson Cancer Center initiative
.
Clin Cancer Res
2012
;
18
:
6373
83
.
5.
Muller
PA
,
Vousden
KH
. 
Mutant p53 in cancer: new functions and therapeutic opportunities
.
Cancer Cell
2014
;
25
:
304
17
.
6.
Said
R
,
Hong
DS
,
Warneke
CL
,
Lee
JJ
,
Wheler
JJ
,
Janku
F
, et al
P53 mutations in advanced cancers: clinical characteristics, outcomes, and correlation between progression-free survival and bevacizumab-containing therapy
.
Oncotarget
2013
;
4
:
705
14
.
7.
Schwaederle
M
,
Lazar
V
,
Validire
P
,
Hansson
J
,
Lacroix
L
,
Soria
JC
, et al
VEGF-A expression correlates with TP53 mutations in non-small cell lung cancer: implications for antiangiogenesis therapy
.
Cancer Res
2015
;
75
:
1187
90
.
8.
Wheler
J
,
Yelensky
R
,
Stephen
B
,
Hong
D
,
Zinner
R
,
Subbiah
V
, et al
Prospective study comparing outcomes in patients with advanced malignancies on molecular alteration-matched versus non-matched therapy
.
J Clin Oncol
33
; 
2015
(
suppl; abstr 11019
).
9.
Oken
MM
,
Creech
RH
,
Tormey
DC
,
Horton
J
,
Davis
TE
,
McFadden
ET
, et al
Toxicity and response criteria of the Eastern Cooperative Oncology Group
.
Am J Clin Oncol
1982
;
5
:
649
55
.
10.
Frampton
GM
,
Fichtenholtz
A
,
Otto
GA
,
Wang
K
,
Downing
SR
,
He
J
, et al
Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing
.
Nat Biotechnol
2013
;
31
:
1023
31
.
11.
Mann
HB
,
Whitney
DR
. 
On a test of whether one of two random variables is stochastically larger than the other
.
Ann Math Statist
1947
;
18
:
50
60
.
12.
Kaplan
EL
,
Meier
P
. 
Nonparametric estimation from incomplete observations
.
J Am Stat Assoc
1958
;
53
:
457
81
.
13.
Cox
DR
. 
Regression models and life-tables
.
J R Stat Soc B Stat Methodol
1972
;
34
:
187
220
.
14.
Garrido-Laguna
I
,
Janku
F
,
Vaklavas
C
,
Falchook
GS
,
Fu
SQ
,
Hong
DS
, et al
Validation of the royal marsden hospital prognostic score in patients treated in the phase I clinical trials program at the MD Anderson Cancer Center
.
Cancer
2012
;
118
:
1422
8
.
15.
Arkenau
HT
,
Barriuso
J
,
Olmos
D
,
Ang
JE
,
de Bono
J
,
Judson
I
, et al
Prospective validation of a prognostic score to improve patient selection for oncology phase I trials
.
J Clin Oncol
2009
;
27
:
2692
6
.
16.
Wheler
J
,
Tsimberidou
AM
,
Hong
D
,
Naing
A
,
Falchook
G
,
Piha-Paul
S
, et al
Survival of 1,181 patients in a phase I clinic: the MD Anderson Clinical Center for targeted therapy experience
.
Clin Cancer Res
2012
;
18
:
2922
9
.
17.
Wheler
J
,
Yelensky
R
,
Falchook
G
,
Kim
KB
,
Hwu
P
,
Tsimberidou
AM
, et al
Next-generation sequencing of exceptional responders with BRAF-mutant melanoma: implications for sensitivity and resistance
.
BMC Cancer
2015
;
15
:
61
.
18.
Janku
F
,
Wheler
JJ
,
Naing
A
,
Falchook
GS
,
Hong
DS
,
Stepanek
VM
, et al
PIK3CA mutation H1047R is associated with response to PI3K/AKT/mTOR signaling pathway inhibitors in early-phase clinical trials
.
Cancer Res
2013
;
73
:
276
84
.
19.
Farhang Ghahremani
M
,
Goossens
S
,
Haigh
JJ
. 
The p53 family and VEGF regulation: "It's complicated."
Cell Cycle
2013
;
12
:
1331
2
.
20.
Shangary
S
,
Wang
SM
. 
Small-molecule inhibitors of the MDM2-p53 protein-protein interaction to reactivate p53 function: a novel approach for cancer therapy
.
Annu Rev Pharmacol Toxicol
2009
;
49
:
223
41
.
21.
Hirai
H
,
Iwasawa
Y
,
Okada
M
,
Arai
T
,
Nishibata
T
,
Kobayashi
M
, et al
Small-molecule inhibition of Wee1 kinase by MK-1775 selectively sensitizes p53-deficient tumor cells to DNA-damaging agents
.
Mol Cancer Ther
2009
;
8
:
2992
3000
.
22.
Blanden
AR
,
Yu
X
,
Loh
SN
,
Levine
AJ
,
Carpizo
DR
. 
Reactivating mutant p53 using small molecules as zinc metallochaperones: awakening a sleeping giant in cancer
.
Drug Discov Today
2015
;
20
:
1391
7
.
23.
Bieging
KT
,
Mello
SS
,
Attardi
LD
. 
Unravelling mechanisms of p53-mediated tumour suppression
.
Nat Rev Cancer
2014
;
14
:
359
70
.
24.
Blandino
G
,
Deppert
W
,
Hainaut
P
,
Levine
A
,
Lozano
G
,
Olivier
M
, et al
Mutant p53 protein, master regulator of human malignancies: a report on the Fifth Mutant p53 Workshop
.
Cell Death Differ
2012
;
19
:
180
3
.
25.
Rivlin
N
,
Brosh
R
,
Oren
M
,
Rotter
V
. 
Mutations in the p53 tumor suppressor gene: important milestones at the various steps of tumorigenesis
.
Genes Cancer
2011
;
2
:
466
74
.
26.
Goldstein
I
,
Marcel
V
,
Olivier
M
,
Oren
M
,
Rotter
V
,
Hainaut
P
. 
Understanding wild-type and mutant p53 activities in human cancer: new landmarks on the way to targeted therapies
.
Cancer Gene Ther
2011
;
18
:
2
11
.
27.
Oren
M
,
Rotter
V
. 
Mutant p53 gain-of-function in cancer
.
Cold Spring Harb Perspect Biol
2010
;
2
:
a001107
.
28.
Gabrilovich
D
,
Ishida
T
,
Oyama
T
,
Ran
S
,
Kravtsov
V
,
Nadaf
S
, et al
Vascular endothelial growth factor inhibits the development of dendritic cells and dramatically affects the differentiation of multiple hematopoietic lineages in vivo
.
Blood
1998
;
92
:
4150
66
.
29.
Gabrilovich
DI
,
Ishida
T
,
Nadaf
S
,
Ohm
JE
,
Carbone
DP
. 
Antibodies to vascular endothelial growth factor enhance the efficacy of cancer immunotherapy by improving endogenous dendritic cell function
.
Clin Cancer Res
1999
;
5
:
2963
70
.
30.
Pfister
NT
,
Fomin
V
,
Regunath
K
,
Zhou
JY
,
Zhou
W
,
Silwal-Pandit
L
, et al
Mutant p53 cooperates with the SWI/SNF chromatin remodeling complex to regulate VEGFR2 in breast cancer cells
.
Genes Dev
2015
;
29
:
1298
315
.
31.
Qin
G
,
Kishore
R
,
Dolan
CM
,
Silver
M
,
Wecker
A
,
Luedemann
CN
, et al
Cell-cycle regulator E2F1 modulates angiogenesis via p53-dependent transcriptional control of VEGF
.
Proc Natl Acad Sci U S A
2006
;
103
:
11015
20
.
32.
Pal
S
,
Datta
K
,
Mukhopadhyay
D
. 
Central role of p53 on regulation of vascular permeability factor/vascular endothelial growth factor (VPF/VEGF) expression in mammary carcinoma
.
Cancer Res
2001
;
61
:
6952
7
.
33.
Fu
S
,
Hou
MM
,
Naing
A
,
Janku
F
,
Hess
K
,
Zinner
R
, et al
Phase I study of pazopanib and vorinostat: a therapeutic approach for inhibiting mutant p53-mediated angiogenesis and facilitating mutant p53 degradation
.
Ann Oncol
2015
;
26
:
1012
8
.
34.
Meadows
KL
,
Hurwitz
HI
. 
Anti-VEGF therapies in the clinic
.
Cold Spring Harb Perspect Med
2012
;
2
:
pii
:
a006577
.
35.
Ricci
V
,
Ronzoni
M
,
Fabozzi
T
. 
Aflibercept a new target therapy in cancer treatment: a review
.
Crit Rev Oncol Hematol
2015
;
96
:
569
76
.
36.
Kurzrock
R
,
Sherman
SI
,
Ball
DW
,
Forastiere
AA
,
Cohen
RB
,
Mehra
R
, et al
Activity of XL184 (Cabozantinib), an oral tyrosine kinase inhibitor, in patients with medullary thyroid cancer
.
J Clin Oncol
2011
;
29
:
2660
6
.
37.
Zakharia
Y
,
Zakharia
K
,
Rixe
O
. 
Axitinib: from preclinical development to future clinical perspectives in renal cell carcinoma
.
Expert Opin Drug Discov
2015
;
10
:
925
35
.
38.
Smyth
EC
,
Tarazona
N
,
Chau
I
. 
Ramucirumab: targeting angiogenesis in the treatment of gastric cancer
.
Immunotherapy
2014
;
6
:
1177
86
.
39.
Ferrara
N
,
Hillan
KJ
,
Gerber
HP
,
Novotny
W
. 
Discovery and development of bevacizumab, an anti-VEGF antibody for treating cancer
.
Nat Rev Drug Discov
2004
;
3
:
391
400
.
40.
Welsh
SJ
,
Fife
K
. 
Pazopanib for the treatment of renal cell carcinoma
.
Future Oncol
2015
;
11
:
1169
79
.
41.
Hong
D
,
Kurzrock
R
,
Wheler
JJ
,
Naing
A
,
Falchook
GS
,
Fu
S
, et al
Phase I dose-escalation study of the multikinase inhibitor lenvatinib in patients with advanced solid tumors and in an expanded cohort of patients with melanoma
.
Clin Cancer Res
2015
;
21
:
4801
10
.
42.
Hoy
SM
. 
A review of its use in adults with chronic myeloidlLeukaemia or philadelphia chromosome-positive acute lymphoblastic leukaemia
.
Drugs
2014
;
74
:
793
806
.
43.
Keating
GM
,
Santoro
A
. 
Sorafenib a review of its use in advanced hepatocellular carcinoma
.
Drugs
2009
;
69
:
223
40
.
44.
Le Tourneau
C
,
Raymond
E
,
Faivre
S
. 
Sunitinib: a novel tyrosine kinase inhibitor. A brief review of its therapeutic potential in the treatment of renal carcinoma and gastrointestinal stromal tumors (GIST)
.
Ther Clin Risk Manag
2007
;
3
:
341
8
.
45.
Koehler
K
,
Liebner
D
,
Chen
JL
. 
TP53 mutational status is predictive of pazopanib response in advanced sarcomas
.
Ann Oncol
2016
;
27
:
539
43
.
46.
Vidwans
SJ
,
Turski
ML
,
Janku
F
,
Garrido-Laguna
I
,
Munoz
J
,
Schwab
R
, et al
A framework for genomic biomarker actionability and its use in clinical decision making
.
Oncoscience
2014
;
1
:
614
23
.
47.
Janku
F
,
Hong
DS
,
Fu
S
,
Piha-Paul
SA
,
Naing
A
,
Falchook
GS
, et al
Assessing PIK3CA and PTEN in early-phase trials with PI3K/AKT/mTOR inhibitors
.
Cell Rep
2014
;
6
:
377
87
.