Purpose:

To examine whether overall survival (OS) differs for male and female patients with advanced soft-tissue sarcoma (STS).

Experimental Design:

The study included patients from Kaiser Permanente Northern California and Stanford Cancer Center with grade 2 and 3 locally advanced or metastatic STS whose tumor underwent next-generation sequencing. We used Cox regression modeling to examine association of sex and OS adjusting for other important factors.

Results:

Among 388 eligible patients, 174 had leiomyosarcoma (LMS), 136 had undifferentiated pleomorphic sarcoma (UPS), and 78 had liposarcoma. OS for male versus female patients appeared to be slightly better among the full cohort [HR = 0.89; 95% confidence interval (CI), 0.66–1.20]; this association appeared to be stronger among the subsets of patients with LMS (HR = 0.76; 95% CI, 0.39–1.49) or liposarcoma (HR = 0.74; 95% CI, 0.32–1.70). Better OS for male versus female patients was also observed among all molecular subgroups except mutRB1 and mutATRX, especially among patients whose tumor retained wtTP53 (HR = 0.73; 95% CI, 0.44–1.18), wtCDKN2A (HR = 0.85; 95% CI, 0.59–1.23), wtRB1 (HR = 0.73; 95% CI, 0.51–1.04), and among patients whose tumor had mutPTEN (HR = 0.37; 95% CI, 0.09–1.62). OS also appeared to be better for males in the MSK-IMPACT and TCGA datasets.

Conclusions:

A fairly consistent pattern of apparent better OS for males across histologic and molecular subgroups of STS was observed. If confirmed, our results could have implications for clinical practice for prognostic stratification and possibly treatment tailoring as well as for future clinical trials design.

Translational Relevance

It is not clear if there is a differential prognosis between male and female patients with advanced soft-tissue sarcoma. Our analysis of a cohort of combined Stanford Cancer Center and Kaiser Permanente patients with advanced soft tissue sarcoma showed that male patients appeared to have better prognosis. This difference appeared to exist in multiple sarcoma histology subtypes and molecular subgroups defined by the most common comutations including p53, PTEN, and CDKN2A, and was especially evident among patients whose tumor retained wild-type TP53, wild-type CDKN2A, or wild-type RB1 or harbored a PTEN mutation. We also found that overall survival appeared to be better for males than females in the MSK-IMPACT and The Cancer Genome Atlas datasets. Our study may have implications in prognostic stratification and treatment tailoring for patients with advanced soft-tissue sarcoma based on sex.

The most common histologic subtypes of soft-tissue sarcoma (STS) include leiomyosarcoma (LMS), undifferentiated pleomorphic sarcoma (UPS), and liposarcoma (1). Different histologic subtypes share some common genomic alterations but may contain unique changes. For example, LMS and UPS frequently contain TP53 (mutTP53), CDKN2A (mutCDKN2A), RB1 (mutRB1), PTEN (mutPTEN), and ATRX (mutATRX) deletions or mutations but without a unique driver mutation, while dedifferentiated liposarcoma is characterized by near-universal MDM2 and CDK4 amplifications and a relatively low frequency of mutTP53 (2, 3).

The treatment options for advanced STS are limited, with doxorubicin with or without ifosfamide, and gemcitabine and docetaxel combination in the first line, and pazopanib as one of the later lines (4–7). These agents are deployed to treat nearly every intermediate to high-grade advanced STS with a modest response rate (1, 8–10). The response to immune checkpoint inhibitors is also poor in most STSs with a couple of exceptions (alveolar soft part sarcoma, cutaneous angiosarcoma, etc.; refs. 10–16). How the common genomic alterations affect the prognosis of advanced STS has not been well studied. Previous studies had suggested that patients with uterine leiomyosarcoma (uLMS) might have worse overall survival (OS) than patients with extra-uterine leiomyosarcoma (extra-uLMS; ref. 17). However, it is not clear if there is a differential prognosis between male and female patients in other histologic subtypes and in advanced STS as a whole.

This study examined survival in a cohort of adult patients with advanced STS from Kaiser Permanente Northern California (KPNC) and Stanford Cancer Center whose tumors had undergone next-generation sequencing (NGS), and aimed to understand if prognosis differs for male and female patients overall and among different histologic subtypes or molecular subgroups.

Study population

Our cohort included 388 eligible patients (190 from KPNC and 198 from Stanford Cancer Center) with grade 2 or higher locally advanced (unresectable) or metastatic LMS, liposarcoma, and UPS. The KPNC cohort had NGS performed using StrataNGS (Ann Arbor, Michigan) from November 2017 to June 2022 and the Stanford cohort had NGS performed by one of multiple commercial laboratories from February 2015 to April 2022 (see below). Patient data on demographics, Charlson comorbidity index (CCI), performance status (PS), and receipt of systemic therapy were obtained from the electronic medical record (Epic) and cancer registry database. CCI was based on the 12-month period prior to diagnosis of locally advanced or metastatic STS. This study was approved by the KPNC and Stanford Cancer Center institutional review boards with waiver of consent, and was conducted according to International Ethical Guidelines for Biomedical Research Involving Human Subjects (CIOMS).

Next-generation sequencing

StrataNGS of advanced malignancies is currently a 429-gene, pan-solid tumor, NGS assay for formalin-fixed paraffin-embedded (FFPE) tumor tissue, performed on co-isolated DNA and RNA (18). ATRX was included into StrataNGS panel in August 2020. For the Stanford cohort, NGS was performed by FoundationOne (Foundation Medicine; refs. 19, 20), Tempus (Tempus Labs, Inc; refs. 20, 21), and Altera (Natera, Inc; ref. 22). All platforms evaluated the most common genomic alterations of interest, including TP53, CDKN2A, RB1, PTEN, and ATRX. MutCDKN2A includes CDKN2A deletion, mutations, and CDK4 and CCND1 amplification.

Histology

The study included only grade 2 or higher locally advanced (unresectable) or metastatic LMS, liposarcoma, and UPS (either de novo or recurrent). We excluded grade 1 histology including well-differentiated liposarcoma and low-grade myxoid liposarcoma. LMS included uterine LMS (uLMS) and extra-uterine LMS (extra-uLMS), UPS, and liposarcoma. Liposarcoma included pleomorphic liposarcoma, grade 2 and 3 myxoid and dedifferentiated liposarcoma. UPS included unspecified high-grade sarcoma.

Treatment

Treatment included administration of chemotherapy, targeted therapeutics (such as pazopanib), and checkpoint inhibitors after a patient was diagnosed with locally advanced or metastatic disease.

Statistical analysis

OS was measured from the date of diagnosis of locally advanced or metastatic STS to the date of death or end of study follow-up (July 28, 2022 for KPNC cohort and December 27, 2022 for Stanford cohort), whichever came first. We used Pearson χ2 test to assess differences in distributions of demographics and TP53, CDKN2A, RB1, PTEN, and ATRX mutations. We used the one-way ANOVA test to assess differences in continuous variables. We used Kaplan–Meier plot (log-rank test) to perform unadjusted (univariate) OS analysis and estimate median OS. The number of patients at risk in the Kaplan–Meier OS curves accounted for delayed entry into the cohort at the time of receipt of NGS results (i.e., left truncation, with median study entry of 7.7 months post-diagnosis; ref. 23). Cox proportional hazards regression modes were used to estimate HRs and 95% confidence interval (CI) for the association between histology or sex and OS, adjusted for covariates. Time since diagnosis of advanced STS was the time scale used in the regression models, allowing for delayed entry into the cohort (23). Covariates included in our main regression models (and unless otherwise stated) were age (continuous), sex (male, female), ethnicity (non-Hispanic White, Black, Asian, Hispanic, other/unknown), PS (0 to 1, 2, 3 to 4), CCI (continuous), and treatment received (yes, no). We examined the effect of specific mutations in a model that included all five mutations simultaneously: TP53 (yes, no), CDKN2A (yes, no), RB1 (yes, no), PTEN (yes, no), and ATRX (yes, no, unknown) mutations, as well as three different histologic types including LMS (yes, no), UPS (yes, no), and liposarcoma (yes, no). The statistical analysis was performed using SAS software version 9.4, R (R Core Team, 2020).

Data availability

KPNC Institutional Review Board has not provided approval for StrataNGS data on individual patients used in this study to be placed in a public access repository. However, researchers can request access to use this study data by contacting the DOR Data Sharing Workgroup at DOR-DataSharingWorkgroup@kp.org.

Demographics

Patients with LMS appeared to be younger than patients with UPS and liposarcoma, had better PS and CCI, higher percent of receiving treatment, more mutTP53, fewer mutCDKN2A, and more mutRB1, mutPTEN, and mutATRX (Table 1). Compared with male patients, female patients were younger, had better CCI, more LMS, fewer mutCDKN2A, and more mutRB1 and mutATRX (Table 2). The demographics of patients with five most common co-mutations including mutTP53, mutCDKN2A, mutRB1, mutPTEN and mutATRX are shown in the data supplement (Supplementary Tables S1–S5). There was no substantial OS difference between the Stanford and KPNC cohort (HR = 1.12; 95% CI, 0.79–1.61; Supplementary Fig. S1).

OS by histologic subtypes

The OS of each histologic subtype was examined. Patients with LMS had the best OS. Compared with LMS, OS of patients with liposarcoma was modestly worse (HR = 1.26; 95% CI, 0.78–2.05) and OS of UPS was substantially worse (HR = 1.66; 95% CI, 1.17–2.36; Fig. 1A). In unadjusted analysis using Kaplan–Meier plot, the median OS for LMS was 29.5 months, 15.4 months for liposarcoma, and 9.2 months for UPS (P = 0.003 for UPS versus LMS and 0.19 for liposarcoma vs. LMS; Fig. 1B). We compared the OS between uLMS and extra-uLMS and found that OS of uLMS was worse than OS of extra-uLMS [HR = 1.24, (95% CI, 0.71–2.17); Fig. 1A], with median OS of 19.0 months for uLMS and 42.7 months for extra-uLMS (P = 0.18; Fig. 1C).

Differential OS between male and female patients by histologic subtypes

The OS of male versus female patients in the entire cohort and among subgroups based on histologic subtypes was examined. OS of male patients appeared to be better than OS of female patients among the entire cohort (HR = 0.89; 95% CI, 0.66–1.20) and this OS difference appeared to be stronger among patients with LMS (HR = 0.76; 95% CI, 0.39–1.49) or liposarcoma (HR = 0.74; 95% CI, 0.32–1.70), and weaker among patients with UPS (HR = 0.94; 95% CI, 0.59–1.48; Fig. 2A). We examined the MSK-IMPACT dataset to determine if there was an OS difference between male and female patients (24). In the cohort that included patients with UPS, LMS (both uLMS and extra-uLMS), and liposarcoma (including myxoid liposarcoma, pleomorphic liposarcoma but not dedifferentiated liposarcoma because 80% of dedifferentiated liposarcoma cases were labeled primary non-metastatic), OS of female (n = 314) versus male patients (n = 155) appeared to be worse (P = 0.09; Fig. 2B). We examined The Cancer Genome Atlas (TCGA) dataset for OS of patients with dedifferentiated liposarcoma and found that OS of female patients versus male patients also appeared to be worse (P = 0.07; Fig. 2C).

Differential OS between male and female patients by molecular subtypes

The OS of patients with individual co-mutation in comparison to their wild-type counterpart was examined which showed that OS of mutTP53 versus wtTP53 was slightly better (HR = 0.92; 95% CI, 0.66–1.28), OS of mutCDKN2A versus wtCDKN2A was worse (HR = 1.39; 95% CI, 0.93–2.07), OS of mutRB1 versus wtRB1 (HR = 1.33; 95% CI, 0.95–1.87) and OS of mutPTEN versus wtPTEN (HR = 1.69; 95% CI, 1.13–2.52) were also worse, and OS of mutATRX versus wtATRX was not substantially different (HR = 1.07; 95% CI, 0.68–1.67; Fig. 3A).

The OS between male and female patients among molecular subtypes was examined. Interestingly, OS of male versus female patients was substantially better among patients with wtTP53 (HR = 0.73; 95% CI, 0.44–1.18) but only slightly better among patients with mutTP53 (HR = 0.94; 95% CI, 0.62–1.42; Fig. 3B). OS of male versus female patients was better among patients with wtCDKN2A (HR = 0.85; 95% CI, 0.59–1.23) and only slightly better among patients with mutCDKN2A (HR = 0.94; 95% CI, 0.50–1.77; Fig. 3B). OS of male versus female patients was substantially better among patients with wtRB1 (HR = 0.73; 95% CI, 0.51–1.04), but worse among patients with mutRB1 (HR = 1.24; 95% CI, 0.65–2.40; Fig. 3B). OS of male versus female patients was slightly better among patients with wtPTEN (HR = 0.93; 95% CI, 0.67–1.27) but substantially better among patients with mutPTEN (HR = 0.37; 95% CI, 0.09–1.62). OS of male versus female patients was modestly better among patients with wtATRX (HR = 0.85; 95% CI, 0.57–1.29) but substantially worse among patients with mutATRX (HR = 8.85; 95% CI, 0.85–92.30; Fig. 3B).

In this study using the combined KPNC and Stanford cohort of 388 patients with advanced LMS, UPS, and liposarcoma, we have uncovered several intriguing findings. First, OS of male versus female patients appeared to be better among the entire cohort, among histologic subtypes, and among most of the molecular subgroups except mutRB1 and mutATRX. Our results also appear to be consistent with the findings from the OS data extracted from both TCGA and the MSK-IMPACT datasets showing that male patients had better OS than female patients (2, 24). The worse prognosis with mutCDKN2A versus wtCDKN2A is consistent with our previous studies (5, 25). However, to our knowledge, the worse OS with mutRB1 versus wtRB1 and with mutPTEN versus wtPTEN in advanced STS has not been previously reported. In a Notch-driven dedifferentiated liposarcoma mouse model, concordant PTEN deletion accelerated tumor progression (26). PTEN mutation has also been shown to be associated with immune evasion (27).

The better OS of male versus female patients in LMS could be partly related to the fact that uLMS had worse OS than extra-uLMS. However, worse OS of female versus male patients among the entire cohort and among patients with UPS and liposarcoma independently is likely related to other factors that remain unclear. The consistency of our results with that of TCGA and the MSK-IMPACT datasets further supports the validity of our dataset (2, 24). It is not clear how this sex disparity occurred mechanistically, however, if confirmed by additional studies, such finding could have important implications in clinical practice including prognostic stratifications and sex-based treatment tailoring (such as intensities or duration, etc.) as well as implications for future STS clinical trials design.

Epidemiologic studies have generally observed that in most cancer types female patients have better OS than male patients (28). A Korean registry data study showed that overall female patients had approximately 20% better OS than male patients in soft tissue cancers, however, for patients with metastatic soft tissue cancer, the 5-year OS was slightly better for male patients compared with female patients (29). In this study it was also found that male patients had better OS than female patients in colorectal, laryngeal, kidney, and bladder cancer (29). Better OS for male versus female patients in metastatic colorectal, kidney, and bladder cancer was also found in a SEER database study (30). In a separate SEER database (between January 1975 and December 2016) study in patients with synovial sarcoma it was found that male patients had worse OS than female patients, (31) though in our previous synovial sarcoma cohort study we did not detect an obvious OS difference between male and female patients (32, 33). In another SEER database study on head and neck sarcoma, male patients were found to have higher risk of cause-specific death than female patients (34). The most common histologic subtypes of head and neck sarcoma in this study were UPS, rhabdomyosarcoma, osteosarcoma, and Ewing sarcoma (34). In a large European study using 1.6 million population-based EUROCARE-4 cancer cases, it was found that overall female patients had approximately 6% better OS than male patients based on a relative excess risk (RER) model (35). When separated into two different age groups, female patients had approximately 23% better OS than male patients for the age group between 15 to 54 (RER = 0.77), but approximately 4% worse (RER = 1.04) for the age group between 55 and 99 (35).

The better OS of male versus female patients among most of the molecular subgroups in our study is also intriguing. Our results appear to suggest that male patients did particularly well compared with female patients when their tumor retained wtTP53, wtCDKN2A, wtRB1 or wtATRX. Interestingly, male patients with mutPTEN had substantially better OS than female patients with mutPTEN. The mechanism of such a molecularly dependent differential OS between male and female patients is not clear and warrants further investigation. Multiple factors including tumor biology, timely diagnosis, treatment modalities, as well as survivorship care may be involved. Further understanding the survival difference between male and female patients may provide additional insight for improving the outcomes of patients with advanced high-grade soft tissue sarcoma.

Our study has several strengths. Our sample size is relatively large. In addition, our cohort comprised of patients from two large institutions under the care of multi-specialty care centers. Further, our study was focused on the three most common STS histologic subtypes to minimize heterogeneity. Moreover, our cohort included the five most common genomic alterations which were adjusted for Cox regression modeling along with other appropriate variables. Our study also has limitations. First, it is a retrospective study and some patients did not have NGS performed at the time of diagnosis of advanced disease until months later. Nonetheless, we used appropriate statistical methods to address this issue (23). Second, only approximately two-thirds of patients had known ATRX mutation status. Third, the sample size of some subgroups was small and several associations did not reach statistical significance and thus could be due to chance. However, the consistency of findings in the KPNC/Stanford cohort, along with the observation of better OS in males in the MSK-IMPACT and TCGA, are very suggestive of worse OS for females with STS.

In summary, our study suggests that female patients had worse OS than male patients with advanced LMS, UPS, and liposarcoma among the entire cohort and among most of the molecular subgroups. Our results were also consistent with the data extracted from TCGA and the MSK-IMPACT datasets. If further confirmed by additional studies, our results could have implications in clinical practice and in future clinical trial designs based on sex and molecular subgroups and may provide insight for understanding the molecular mechanisms of STS.

M. Pan reports personal fees from Aadi Bioscience and Boehringer Ingelheim outside the submitted work. L.A. Habel reports grants from Strata Oncology outside the submitted work. K.N. Ganjoo reports other support from Daiichi Sankyo outside the submitted work. No disclosures were reported by the other authors.

M. Pan: Conceptualization, resources, data curation, formal analysis, supervision, validation, investigation, methodology, writing–original draft, writing–review and editing. M.Y. Zhou: Data curation, formal analysis, validation, methodology, writing–review and editing. C. Jiang: Data curation, software, formal analysis, investigation, methodology, writing–review and editing. Z. Zhang: Resources, software, formal analysis, investigation, writing–review and editing. N.Q. Bui: Resources, investigation, writing–review and editing. J. Bien: Data curation. A. Siy: Data curation. N. Achacoso: Data curation. A.V. Solorzano: Data curation. P. Tse: Data curation. E. Chung: Data curation. S. Thomas: Data curation, investigation, writing–review and editing. L.A. Habel: Conceptualization, resources, data curation, software, formal analysis, supervision, validation, investigation, methodology, writing–review and editing. K.N. Ganjoo: Conceptualization, resources, data curation, formal analysis, supervision, validation, investigation, methodology, writing–review and editing.

We thank M. van de Rijn, E. Moding, and A. Kalbasi for helpful comments. This study was supported by The Permanente Medical Group and Stanford University. No external funding was obtained for this study.

The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.

Note: Supplementary data for this article are available at Clinical Cancer Research Online (http://clincancerres.aacrjournals.org/).

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