Purpose: Checkpoint inhibitors demonstrate salutary anticancer effects, including long-term remissions. PD-L1 expression/amplification, high mutational burden, and mismatch repair deficiency correlate with response. We have, however, observed a subset of patients who appear to be “hyperprogressors,” with a greatly accelerated rate of tumor growth and clinical deterioration compared with pretherapy, which was also recently reported by Institut Gustave Roussy. The current study investigated potential genomic markers associated with “hyperprogression” after immunotherapy.

Experimental Design: Consecutive stage IV cancer patients who received immunotherapies (CTLA-4, PD-1/PD-L1 inhibitors or other [investigational] agents) and had their tumor evaluated by next-generation sequencing were analyzed (N = 155). We defined hyperprogression as time-to-treatment failure (TTF) <2 months, >50% increase in tumor burden compared with preimmunotherapy imaging, and >2-fold increase in progression pace.

Results: Amongst 155 patients, TTF <2 months was seen in all six individuals with MDM2/MDM4 amplification. After anti-PD1/PDL1 monotherapy, four of these patients showed remarkable increases in existing tumor size (55% to 258%), new large masses, and significantly accelerated progression pace (2.3-, 7.1-, 7.2- and 42.3-fold compared with the 2 months before immunotherapy). In multivariate analysis, MDM2/MDM4 and EGFR alterations correlated with TTF <2 months. Two of 10 patients with EGFR alterations were also hyperprogressors (53.6% and 125% increase in tumor size; 35.7- and 41.7-fold increase).

Conclusions: Some patients with MDM2 family amplification or EGFR aberrations had poor clinical outcome and significantly increased rate of tumor growth after single-agent checkpoint (PD-1/PD-L1) inhibitors. Genomic profiles may help to identify patients at risk for hyperprogression on immunotherapy. Further investigation is urgently needed. Clin Cancer Res; 23(15); 4242–50. ©2017 AACR.

Translational Relevance

Unique immunotherapy-induced response patterns have been observed, including a report of hyperprogression. Our patients showed remarkably accelerated tumor growth rate after anti-PD1/PDL1 monotherapy. Hyperprogressors harbored MDM2/4 or EGFR alterations, which independently correlated with time-to-treatment failure <2 months, suggesting the need for caution in the presence of these genomic profiles.

Immune checkpoint inhibitors have become a standard of care for multiple cancer types. These agents are considered transformative, at least in part because of the phenomenon of hyper-responders that refers to a subset of patients with long-term complete remission. Overall, however, response rates for single agent immune checkpoint inhibitors in solid malignancies range from 20% to 40% (1–3). There are now several biomarkers partially capable of predicting response: PD-L1 expression/amplification, high tumor mutational burden, and mismatch repair gene defects (4–6). Recently, potential markers for acquired resistance have also been reported: loss-of-function mutations in the Janus kinase 1 (JAK1) or JAK2 and beta-2-microglobulin truncation (7). Immunotherapy may also result in a unique response pattern known as pseudoprogression where tumors initially appear larger on imaging, but subsequently regress (8). Importantly, Champiat and colleagues (9) also documented “hyperprogressive disease,” which they noted in 9% of patients treated with immune checkpoint inhibitors. We also recently identified a unique subset of patients whose disease paradoxically accelerated on immunotherapy. Herein, we describe our cohort of hyperprogressors and the genomic profiles that they harbor.

We describe all six patients with stage IV cancer and documented MDM2 gene family amplification who received immunotherapy (see genomic profiles in Supplementary Table S1)

Case #1

A 73-year-old man with bladder cancer metastatic to the liver and lymph nodes (high tumor mutational burden with multiple alterations including MDM2 amplification) was started on the anti–PD-L1 agent atezolizumab (10). Prior therapies included gemcitabine/cisplatinum, as well as a trial of lenvatinib and olaparib, on which he had shown slow progression. Restaging imaging done 1.9 months after starting atezolizumab showed a 258% increase in size of the liver masses from preimmunotherapy imaging as well as new liver metastases that were highly positron emission tomography (PET)- fluorodeoxyglucose (FDG) avid (Figs. 1A and 2). Repeat imaging one month later confirmed progression. Patient lost weight, developed tumor fevers and progressive syndrome of inappropriate antidiuretic hormone (SIADH) and died soon afterwards.

Case #2

A 44-year-old woman with triple-negative breast cancer and MDM2 amplification showed stable lung metastases while receiving only local radiation therapy for brain metastases. However, 1.5 months after starting pembrolizumab, CT scan revealed a 55% increase in the left lung mass as well as new chest wall masses and lymphadenopathy (Fig. 1B).

Case #3

A 65-year-old woman with endometrial stromal sarcoma had progression in liver metastases on targeted therapy over 6 months [cancer antigen 125 (CA125) increase from 11 to 33 U/mL]. Therapy was switched to a trial of nivolumab combined with stereotactic body radiation therapy (SBRT). Soon thereafter, she experienced abdominal pain and new palpable masses. CT imaging (1.5 months post nivolumab) showed rapid progression of liver metastasis plus new bulky abdominal masses (242% increase from preimmunotherapy; Fig. 1C). CA125 also increased from 33 to 1,040 U/mL. Tumor biopsy at the time of progression did not reveal signs of pseudoprogression, including lymphocyte infiltration or tumor necrosis (Supplementary Fig. S1). Two tumor biopsies (2 weeks and 2 months after the initiation of nivolumab) both showed MDM2 amplification.

Case #4

A 50-year-old woman with lung adenocarcinoma harboring KIF5B-RET fusion and MDM2 amplification had gradual progression on Abraxane. This therapy was changed to pembrolizumab. Nine days later, patient presented with severe fatigue, which prompted the physician to obtain CT imaging. Scans showed rapid progression of lung metastases (135% increase from preimmunotherapy; Fig. 1D).

Case #5

A 61-year-old man with lung adenocarcinoma had a genomic profile that demonstrated MDM2 and CDK4 amplification. He completed first-line therapy with carboplatin, paclitaxel and bevacizumab. Surveillance imaging demonstrated new lung disease and the therapy was changed to pembrolizumab. The patient then developed worsening dyspnea and severe generalized fatigue. Although CT scan of the chest showed stable disease, therapy was discontinued due to clinical progression 1.5 months after starting pembrolizumab. Subsequent MRI showed multiple new brain metastases (Fig. 1E).

Case #6

A 62-year-old man with squamous cell carcinoma (hypopharynx) was treated with an OX40 agonist (third-line therapy). However, 1.4 months afterwards, the patient was taken off study due to progressive altered mental status attributed to hyponatremia from tumor-associated SIADH. Although imaging was stable at the time (Fig. 1F), the patient died 3 months later. NGS showed several alterations including MDM4 amplification.

Patients

Two patients (Cases #1 and #3) were seen in the authors' clinic. Observations of rapid progression and genomic commonalities prompted further investigation via database and chart review. Consequently, we analyzed all patients with stage IV cancers who received immunotherapies [CTLA-4, PD-1/PD-L1 inhibitors or other (investigational) agents; March 2011 through July 2016] and had tumor evaluated by NGS (Foundation Medicine) at Moores UCSD Cancer Center (N = 155). When available, clinical information, including lymphocyte count, albumin, lactate dehydrogenase (LDH), number of organ metastases and Eastern Cooperative Oncology Group Performance Status (ECOG PS), were collected (laboratory, radiographic, and ECOG PS information was collected within 4 weeks of the initiation of immunotherapy). Royal Marsden Hospital score (11) and MD Anderson Cancer Center prognostic score (12) were evaluated when available. This study was performed in accordance with the guidelines of the UCSD Internal Review Board (PREDICT protocol; NCT02478931) and the investigational studies for which the patients gave consent.

Comprehensive genomic profiling

NGS was performed with assay panels of 182, 236, or 315 genes according to previously reported methods in a CLIA-certified, CAP-accredited laboratory (https://www.foundationmedicine.com; refs. 13–15). Average sequencing depth of coverage was greater than ×250, with >×100 at >99% of exons. This method of sequencing allows for detection of copy number alterations, gene rearrangements, and somatic mutations with 99% specificity and >99% sensitivity for base substitutions at ≥5 mutant allele frequency and >95% sensitivity for copy-number alterations. A threshold of ≥8 copies was used for gene amplification.

Statistical analysis

Fisher exact test was used to assess the association between categorical variables. Exact conditional logistic regression analysis was used for the multivariate analysis (16). Bootstrapping was performed using random sampling with replacement to create a large number (N = 982) of “phantom samples” known as bootstrap samples. The sample summary is then computed on each of the bootstrap samples. This method can be superior to approaches relying on the asymptotic distribution of the tests that assumes the data come from a normal distribution, allowing the data of the sample study at hand to be used as a surrogate for a larger population. Although cross validation remains the preferred approach for validating predictive models, bootstrapping can be used when the sample size is too small to be split into a training and a validation set and/or there is no independent cross-validation cohort (ref. 17; as is the case in our study).

All tests were two-sided. P values ≤ 0.05 were considered significant. The Fisher's exact test was performed using Graph-Pad Prism version 7.0. Multivariate exact conditional logistic regression was performed with SAS software, version 9.4. Bootstrapping with multiple logistic regression analysis was performed with SPSS version 24.0

Analysis of all patients who received immunotherapy and had molecular profiling (N = 155 patients)

Patient characteristics.

Among the 155 patients with diverse cancers who received immunotherapy and had molecular profiling, 49 (31.6%) had a time-to-treatment failure (TTF) <2 months; 106 (68.4%), TTF ≥2 months. The most common cancers were melanoma [32.9% (51/155)] and NSCLC [24.5% (38/155)]. Most patients received anti-PD-1/PD-L1 agents [65.8% (102/155)] followed by anti-CTLA-4 alone or in combination with anti–PD-1 [22.6% (35/155)]. The most commonly altered gene was TP53 [41.9% (65/155)] followed by CDKN2A/B [29.7% (46/155)] and TERT [23.9% (37/155)]. MDM2/MDM4 amplifications were found in 6 patients (4%; Table 1).

Characteristics associated with outcome after immunotherapies.

Univariate analysis

Patients with melanoma were more likely to have TTF≥2 months (odds ratio, 0.19; P = 0.0002); NSCLC, TTF<2 months (OR, 2.5, P = 0.03). Among types of immunotherapy, patients who received anti–PD-1 or PD-L1 antibodies tended to have TTF<2 months (OR, 2.66; P = 0.02); patients who received anti–CTLA-4 alone or in combination with anti-PD-1 tended to have TTF≥2 months (OR, 0.29; P = 0.01; Table 1). Thirty-nine of 102 patients (38%) treated with anti–PD-1 or PD-L1 monotherapy had TTF<2 months versus 10 of 53 patients (19%) who received other immunotherapy agents (P = 0.02; Table 1). Absolute lymphocyte count and prognostic scores including Royal Marsden Hospital score and MD Anderson Cancer Center prognostic score were not associated with duration of TTF from immunotherapies (Table 1).

Alterations in several genes were associated with favorable clinical outcomes (TTF≥2 months): TERT (OR: 0.42, P = 0.07), PTEN (OR, 0.28; P = 0.10), NF1 (OR, 0.15; P = 0.07), and NOTCH1 (OR, <0.19; P = 0.02). In contrast, EGFR (OR, 10.2; P = 0.002), MDM2/4 (OR, >11.9; P = 0.001) and DNMT3A (OR, 9.33; P = 0.03) alterations were associated with poorer clinical outcomes (TTF<2 months; Table 1).

Multivariate analysis

Variables with p ≤ 0.1 were included in multivariate analysis. A diagnosis of melanoma was significantly associated with TTF≥2 months (P = 0.02). EGFR, MDM2/4 and DNMT3A alterations remained independent predictors of poor clinical outcome (TTF<2 months) with immunotherapies (all P ≤ 0.04; Table 1).

Analysis with bootstrapping method

Bootstrapping was conducted on characteristics with P-value ≤ 0.1 from univariate analysis (Table 1). Diagnoses of melanoma and cutaneous squamous cell carcinoma as well as genomic alteration with NOTCH1 were significantly associated with TTF≥2 months (P = 0.004, P = 0.001, and P = 0.003, respectively; Table 1). In contrast, EGFR, MDM2/4, and DNMT3A alterations were all significant for TTF<2 months (P = 0.004, P = 0.001, and P = 0.04, respectively; Table 1).

Analysis of patients treated with anti–PD-1/PD-L1 monotherapy (N = 102 patients)

Seven of 8 (87.5%) patients harboring EGFR alterations had TTF <2 months; 1 of 8 (12.5%) had TTF ≥2 months (P = 0.005 by univariate analysis). Among five patients with MDM2 amplifications, all had TTF < 2 months (P = 0.007 by univariate analysis; Supplementary Table S2). Three of 4 patients (75%) harboring DNMT3A alteration had TTF <2 months; however, this was not statistically significant (P = 0.15 by univariate analysis). After multivariate analysis, EGFR alterations and MDM2 amplification continue to show significance for correlation with TTF <2 months (P = 0.02). With the bootstrap analysis, both EGFR and MDM2 alterations remained statistically significant (P = 0.014 and 0.001, respectively; Supplementary Table S2). Altogether, six of 13 patients (46%) with EGFR or MDM2 alterations demonstrated hyperprogression (Supplementary Tables S1 and S3).

Hyperprogressors and poor-risk genomic alterations (EGFR, MDM2/4, and DNMT3A).

We defined hyperprogression as TTF<2 months, >50% increase in tumor burden compared with preimmunotherapy imaging that was obtained within 2 months of the initiation of immunotherapy, and >2-fold increase in progression pace. Immune-related response criteria (18) were used for the evaluation of response.

Among six patients with MDM2 family amplifications (N = 5, MDM2; 1, MDM4), all had TTF<2 months. Four of these patients (67%) demonstrated hyperprogression (all with MDM2 amplification) with increases in lesions compared with preimmunotherapy as follows: Case #1, 258%; Case #2, 55%; Case #3, 242%; Case #4, 135% (increase in pace of progression ranging from 2.3-fold to 42.3-fold; Table 1, Fig. 1A–D, Fig. 2; Supplementary Table S1). Of note, Case #3 also had a precipitous rise in CA125 (Fig. 1C). Two additional patients with MDM2/4 alterations had TTF<2 months, but showed only minor progression of target lesions. Both were taken off study early for clinical symptoms suggesting progression. Case #5 (MDM2 amplification) subsequently demonstrated new brain metastases (Fig. 1E). The other patient (MDM4 amplification) showed worsening SIADH despite stable imaging resulting in stopping drug; patient expired 3 months later (Case #6, Fig. 1F).

Among 10 patients with EGFR alterations, 8 had a TTF<2 months (Table 1). Two (20%) demonstrated hyperprogression (increase in lesions of 53.6% and 125% until first re-staging; increase in progression pace of 35.7- and 41.7-fold; Supplementary Table S3 and Supplementary Fig. S2, Cases #11 and #13). Four of 5 patients with DNMT3A alterations had a TTF <2 months (Table 1). Only one patient was evaluable with serial imaging and did not manifest hyperprogression (Supplementary Table S4).

We report that patients with MDM2 family amplifications appear to be at risk of accelerated progression after immunotherapy. Although immunotherapy-induced hyper-responses are a well-known phenomenon, hyperprogression has only recently been described (9, 19, 20). Champiat and colleagues (9) demonstrated that 9% of patients (12/131) showed a ≥2-fold increase in tumor burden when compared with preimmunotherapy imaging. Older age was associated with hyperprogression in that study, but not in ours (Table 1). Genomic profiling was not reported.

In our 155 patients, all six (4%) with MDM2/MDM4 amplification were taken off immunotherapy in less than two months, and four showed a clearly accelerated rate of tumor growth compared with that before treatment (∼2.3 to 42.3-fold increase in progression rate; Figs. 1 and 2). One of our hyperprogressors had a high tumor mutational burden, which is usually associated with response. Another two MDM2/MDM4-amplified patients demonstrated rapid clinical deterioration, with new brain metastases and quickly worsening SIADH, respectively.

A TTF less than two months was also documented in 8 of 10 patients with EGFR alterations and 4 of 5 with DNMT3A alterations. In two patients with EGFR alterations (Supplementary Tables 3 and Supplementary Fig. S2, Cases #11 and #13), imaging data documented hyperprogression (∼36-fold and 42-fold increase in progression pace compared with the 2 months before immunotherapy). The other patients did not have available serial imaging or showed evidence of progression compatible with standard resistance.

MDM2 amplification is found in about 7% of cancers; it inhibits the p53 tumor suppressor (MDM4 is a homolog of MDM2 that interacts with it and also inhibits p53; ref. 21). The exact mechanism linking MDM2 amplification and hyperprogression is unclear (Fig. 1A–D and Fig. 2). However, immune checkpoint inhibitors can lead to elevated interferon (IFN)-γ (22), which in turn activates JAK-STAT signaling (23) resulting in an increase in interferon regulatory factor (IRF)-8 expression (24). IRF8 binds to the MDM2 promoter inducing MDM2 expression (25, 26). It is conceivable that this cascade may not have significant impact when MDM2 is not amplified; however, in the presence of MDM2 amplification, hyperexpression could occur. Other hypotheses are also plausible, including the involvement of a gene that sits on the MDM2 amplicon and is co-amplified with it. Further investigation is crucial. Of note, MDM2 inhibitors are currently in clinical development (27) raising the possibility that a combination strategy could limit hyperprogression on immunotherapy.

EGFR activation is associated with upregulation of PD-1, PD-L1, and CTLA-1, which can drive immune escape (28). This may explain resistance in EGFR-mutated tumors, though it does not explain the hyperprogression seen in two of our patients. Other resistance alterations that have been described include JAK1- or JAK2-inactivating alterations and beta-2-microglobulin truncation (7).

The possibility of hyperprogression has considerable importance for affected patients. Here, we report that specific genomic alterations may be associated with accelerated progression, that is, the presence of MDM2 family amplification or EGFR aberrations. Importantly, however, a preliminary report by Tawbi and colleagues (29) suggests that patients with liposarcomas, a disease that commonly harbors MDM2 amplification, occasionally demonstrated clinical benefit [partial response rate of 11% (1/9)] after immunotherapy. The latter observation indicates that, not unexpectedly, the hyperprogression response pattern does not apply universally to patients with this genomic alteration. It is also plausible that hyperprogression may be more likely in some histologies than in others. Indeed, our patients with MDM2 amplification and hyperprogresssion had bladder, breast, endometrial stromal sarcoma, and lung cancers, and the two patients with EGFR alterations and hyperprogression had adenocarcinoma of the lung. Of note, all of our hyperprogressors were treated with anti-PD1/PDL1 monotherapy, and it remains unknown if other immunotherapy drugs would exhibit similar phenomena. However, in univariate analysis, patients who received an anti-CTLA-4 (alone or combined with anti-PD1/PDL1) were significantly less likely to have a TTF less than two months, and none were hyperprogressors. It is also plausible that MDM2 family amplification is not the marker for hyperprogression but rather there is another gene that resides nearby and is co-amplified, leading to hyperprogression from immunotherapy, or that there is some other co-factor present.

Finally, there were several limitations to our study. For instance, an increased pace of progression in our individual patients was based on comparing progression rate in the first 2 months after immunotherapy treatment to that in the preceding 2 months. However, a prospective randomized study of checkpoint inhibitors versus standard therapy would be required to have an absolutely unbiased estimate of comparative progression rates.

In summary, our observations suggest that patients for whom anti-PD1/PDL1 monotherapy is planned may require genomic testing to determine whether their tumors harbor specific alterations associated with hyperprogression. Individuals with these alterations, if treated with anti-PD1/PDL1 agents, should be closely monitored. Larger studies, validation cohorts, and translational research are urgently needed.

A. Sharabi reports receiving speakers bureau honoraria from Varian Medical Systems, and is a consultant/advisory board member for AstraZeneca. R. Kurzrock is an employee of and holds ownership interest (including patents) in CureMatch, Inc., reports receiving commercial research grants from Foundation Medicine, Genentech, Guardant, Merck Serono, Pfizer, and Sequenom, and is a consultant/advisory board member for Actuate Therapeutics and Xbiotech. No potential conflicts of interest were disclosed by the other authors.

Conception and design: S. Kato, A. Goodman, A. Sharabi, R. Kurzrock

Development of methodology: S. Kato, R. Kurzrock

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): S. Kato, A. Goodman, A. Sharabi

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): S. Kato, D.A. Barkauskas, A. Sharabi, R. Kurzrock

Writing, review, and/or revision of the manuscript: S. Kato, A. Goodman, V. Walavalkar, D.A. Barkauskas, A. Sharabi, R. Kurzrock

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): S. Kato, A. Goodman

Study supervision: R. Kurzrock

Funded in part by the Joan and Irwin Jacobs fund and by National Cancer Institute grant P30 CA016672 (to R. Kurzrock).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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