Identifying Patters of Doxorubicin Sensitivity in Soft Tissue Sarcoma Using Next Generation Sequencing

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Public Health in the Graduate School of The Ohio State University

Manojkumar Bupathi

Graduate Program in Public Health

The Ohio State University

2017

Thesis Committee:

Professor James Chen – Advisor

Professor Philip Binkley

Professor Gregory Otterson

Professor Brian Hilligoss

Manojkumar Bupathi

2017

Abstract

Soft-tissue sarcomas account for less than 1% of new cancer diagnoses in both men and women in the United States. Surgery is offered for localized disease amenable to resection. However, less than 20% of patients present with initially resectable disease, and the 5-year survival rate for this locally advanced/metastatic population is abysmal

(<25%). Sadly, our ability to systemically treat metastatic soft tissue sarcoma chemotherapy remains a case of trial-and-error. Indeed, the National Cancer Center

Network guidelines list nine acceptable single agents and six different combination regimens. Doxorubicin serves as the backbone of most treatment regimens but induces significant tumor regression in only 15-30% of patients. We conducted a retrospective review of patients with leiomyosarcoma who were treated at our institution and had next generation sequencing with FoundationOne. Our aim was to determine if we could determine sensitivity to doxorubicin and other agents that are commonly used to treat

STS based on p53 status. Our data indicates that the type of abnormality in p53 can have prognostic implications with doxorubicin and pazopanib. Further, we performed a cluster analysis to determine if there are any specific patterns in genetic pathways which could identify be used to group patients together. Using the leiomyosarcoma database in TCGA and GENIE, we generated the same clusters for those patients to compare the patients between the datasets. We found that there are similarities among along three datasets. For example, most male patients with leiomyosarcoma fall into cluster 3. The exact ii mechanism of why this happens is currently not known. In addition, patients in cluster 2 in TCGA perform worse compared to our dataset. However, patients in our dataset were treated with targeted therapy suggesting that molecular directed therapy could have better outcomes. Finally, we identified 10 patients with BRCA alterations. Four out of six patients with BRCA2 alterations had the same variance of unknown significance alteration at K33226x. This specific alteration has been associated with increased risk in breast, ovarian, pancreas and lung cancer. Given the high prevalence of this alteration in our cohort, it is important for this be evaluated in other datasets to get a better understanding of its prevalence so that patients can appropriately be screened or counseled regarding their risks. Though precision medicine focuses on individualized care success truly requires a population-based approach and understanding what interventions work on individual need to be compared with data from large, diverse numbers of people to identify population subgroups likely to respond differently to intervention.

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Dedication

This document is dedicated to my family.

Acknowledgments

I would like to express my appreciation to my advisory committee: Dr. James Chen, Dr.

John Hays, and Dr. Philip Binkley. Thanks for giving me the opportunity to be part of the clinical translational research team in the lab. A special thanks to Dr. Chen for his time, dedication, and patience to ensure that I am successful in completing this project and obtaining my degree. Dr. Hays, it has been an honor to work with you and thank you for your advice and for acting as a mentor to me. Also, thanks Dr. Binkley for taking the time to review and assisting in completing my thesis. Dr. Otterson, thank you so much for all of your support and encouragement in completing this Masters while in my fellowship as well as serving on my thesis committee. My gratitude also goes to everyone who works in the lab, there are not enough words to describe your excellent work. Special thanks to

Nicholas Grosenbacher, you were there to help no matter time or day of the week with data analysis.

The most special thanks goes to my best partner and friend, my wife. Maithreye, you gave me your unconditional support and love through all this long process.

Vita

June 2002 ...... East Brunswick High School

January 2005 ...... B.A. Biology, Rutgers University

May 2009 ...... M.D. St. Georges University

July 2009 ...... Post Graduate Training, Department of

Internal Medicine, Case Western University

July 2013 ...... Post Graduate Training, Investigational

Therapeutics, MD Anderson Cancer Center

July 2014 ...... Post Graduate Training, Medical Oncology,

The Ohio State University

Publications

Book Chapter:

Bupathi, M. “Myelodysplastic/Myeloproliferative Neoplasms” in Myelodysplastic

Syndromes, second edition, H.J Deeg, DT Brown, SD Gore, T Haferlach, MM LeBeau, C

Niemeyer, Eds. New York. Springer Publishing, 2013 pp 116-131

Peer-Reviewed Publications

1. Chemotherapy options for Intrahepatic Cholangiocarcinoma. Bupathi M, Bekaii-

Saab T. Hepatobiliary Surgery and Nutrition. 26th Dec 2016.

2. Incidence of infusion reactions to anti-neoplastic agents in early phase clinical trials: The MD Anderson Cancer Center experience. Bupathi M, Hajjar J, Bean S, Fu S,

Hong D, Karp D, Stephen B, Hess K, Meric-Bernstam F, Naing A. Investigational New

Drugs. 29 Sept 2016. doi:10.1007/s10637-016-0395-y

3. Spotlight on bevacizumab in metastatic colorectal cancer: patient selection and perspectives. Bupathi M, Ahn D, Bekaii-Saab T. Gastrointestinal Cancer: Targets and

Therapy. 30 June 2016; doi https://dx.doi.org/10.2147/GICTT.S97740

4. Biomarkers for immune therapy in colorectal cancer: mismatch-repair deficiency and others. Bupathi M, Wu C. J Gastrointest Oncol. 2016;7(5):713-720. doi: 10.21037/ jgo.2016.07.03

5. Modified irinotecan and infusional 5-fluorouracil (mFOLFIRI) in patients with refractory advanced pancreas cancer (APC): a single-institution experience. Bupathi M,

Ahn D, Wu C, Ciombor KK, Stephens JA, Reardon J, Goldstein DA, Bekaii-Saab T.

Medical Oncology. 2016 April; 33(4):37.

vii

6. Clinical next-generation sequencing reveals aggressive cancer biology in adolescent and young adult patients. Subbiah V, Bupathi M, Kato S, Livingston A, Slopis

J, Anderson PM, Hong DS. Oncoscience. 2015 Jul 8; 2(7):646-58.

7. Hepatocellular carcinoma: Where there is unmet need. Bupathi M, Kaseb A,

Meric-Bernstam F, Naing A. Molecular Oncology. Mol Oncol. 2015 Jun 25. pii: S1574-

7891(15)00129-5. doi: 10.1016/j.molonc.2015.06.005.

8. Angiopoietin-2 as a therapeutic target in hepatocellular carcinoma treatment: current perspectives. Bupathi M, Kaseb A, Janku F. OncoTargets and Therapy. 2014

Oct 20;7:1927-32. doi: 10.2147/OTT.S46457

9. SF3B1 haploinsufficiency leads to formation of ring sideroblasts in myelodysplastic syndromes. Visconte V, Rogers HJ, Visconte V, Rogers HJ, Singh J,

Barnard J, Bupathi M, Traina F, McMahon J, Makishima H, Szpurka H, Jankowska A,

Jerez A, Sekeres MA, Saunthararajah Y, Advani AS, Copelan E, Koseki H, Isono K,

Padgett RA, Osman S, Koide K, O'Keefe C, Maciejewski JP, Tiu RV. Blood. 2012 Oct

18;120(16):3173-86. doi: 10.1182/blood-2012-05-430876

10. Mutations in the spliceosome machinery, a novel and ubiquitous pathway in leukemogenesis. Makishima H, Visconte V, Sakaguchi H, Jankowska AM, Abu Kar S,

viii

Jerez A, Przychodzen B, Bupathi M, Guinta K, Afable MG, Sekeres MA, Padgett RA,

Tiu RV, Maciejewski JP. Blood. 2012 Apr 5;119(14):3203-10. doi: 10.1182/blood-2011-

12-399774

11. Esophageal and Gastric T-Cell Lymphoma: A Rare Entity. Sappati R, Bupathi M,

Solomon A, Kyprianou A. Journal of Krishna Institute of Medical Sciences. Jan 2012

12. Aquired Erythrocytosis on Treatment with infliximab for Ankylosing spondyilits.

Antonelli M, Bupathi M, Janakiram M, Hergenroeder P, Khan MA. Journal of

Rheumatology 2011 Mar;38(3):581-3. doi: 10.3899

Fields of Study

Major Field: Public Health

Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgments ...... v

Vita ...... vi

List of Tables ...... xii

List of Figures ...... xiii

Chapter 1: Background ...... 1

Leiomyosarcoma ...... 5

Uterine Leiomyosarcoma ...... 16

Chapter 2: Methods ...... 19

Eligibility Criteria ...... 19

Study Design ...... 19

Endpoint ...... 20

Statistical Analysis ...... 21

FoundationOne ...... 22

Chapter 3: Results ...... 24

Patient Characteristics ...... 24

Efficacy Analysis ...... 25

Chapter 4: Discussion ...... 38

Chapter 5: Public Health Relevance ...... 46

Appendix A: Figures & Tables ...... 49

References ...... 67

List of Tables

Table 1: Patient Characteristics ...... 64

Table 2: Summary of mutations that are seen in patients with BRCA abnormalities ...... 64

Table 3: Summary of patient characteristics for those with BRCA alterations ...... 65

Table 4: Summary of patient characteristics for those with BRCA K3326X alterations .. 65

Table 5: Summary of patient similarities between our dataset, TCGA and GENIE...... 66

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List of Figures

Figure 1: Mutations per patient ...... 49

Figure 2: Total number of mutations ...... 50

Figure 3: p53 and Overall Survival ...... 51

Figure 4: Doxorubicin and p53 ...... 52

Figure 5: Doxorubicin in non-metastatic patients ...... 53

Figure 6: Pazopanib and p53 ...... 54

Figure 7: Gem/tax and p53 ...... 55

Figure 8: BRCA and Overall Survival ...... 56

Figure 9: Heatmap for all LMS patients ...... 57

Figure 10: Cluster 2 in OSU and TCGA ...... 58

Figure 11: Cluster 3 in OSU and TCGA ...... 58

Figure 12: Cluster 6 in OSU and TCGA ...... 59

Figure 13: uLMS vs rest of patients ...... 60

Figure 14: Heatmap for uLMS patients ...... 61

Figure 15: Cluster 2 OSU vs TCGA in uLMS patients ...... 62

Figure 16: Cluster 5 OUS vs TCGA in uLMS patients ...... 63

Figure 17: RB1 in TCGA vs OSu ...... 63

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Chapter 1: Background

Soft tissue sarcomas (STS) are a rare and heterogeneous set of disease which represent 6-

15% of pediatric cancer (<15 years), 11% of adolescent and young adult cancer (15-29) and account for less than 1% of all new adult cancer diagnoses in both men and women with more than 70 different types of histology. In 2017, it is estimated that 12,390 people in the United States will be diagnosed with STS and the heterogeneous nature of the disease poses a therapeutic challenge. The majority of STS arise in the extremities (60%), followed by abdomen and retroperitoneum (20%), abdominal/thoracic wall (5%) and head and neck (5%) (1). The five year overall survival for STS (for all stages) is approximately 50-60% (2).

The most important prognostic factors are tumor location, histology, grade and size.

Tumors that are deep seeded, located underneath the muscular fascia, generally exhibit a more aggressive behavior(2). Further, superficial tumors, located above the muscular fascia, can lead to significant impairment before and after treatment.

There are more than 70 different histological entities within sarcoma with the three most common being liposarcoma (20%), leiomyosarcoma (15%) and undifferentiated pleomorphic sarcoma (15%)(2, 3). Each of the different subtypes has differential degrees of malignancy(4, 5). An example would be a low-grade liposarcoma could recur locally after surgery and rarely metastasize. In contrast, a high-grade leiomyosarcoma can have 1 metastatic disease at presentation. Tumor grade can be another power tool for predicting outcome. For instance, STS, which is localized to the extremity with low and high grade, have a survival rate of 90% and 60% respectively. The size of the tumor can also be important in survival. Smaller tumors (frequently < 5cm) in the extremities have a 10 year survival rate of 80%, which is significantly longer than tumors which are > 10 cm(2).

Wide surgical resection and radiation therapy are the primary modalities of localized disease amenable to resection. Primary tumors are generally treated with surgery and can be grouped with radiation therapy, when the risk of local recurrence is high(6). For some patients, there is some evidence to use neo-adjuvant therapy to obtain better surgical margins as well as decrease chance of local recurrence; however, the data is still evolving

(7-10).

STS typically metastasize to the lungs however depending on the histology there could be preferential metastasis to another organ. For individuals with single or local metastasis, metastasectomy could be considered to improve disease free interval after primary tumor treatment(11, 12). For the majority of patients with metastatic sarcomas, surgery is typically not a suitable option, especially if there are multiple metastatic sites. In this setting, the only option for improved survival is with systemic therapy.

Unfortunately, less than 20% of patients present with initially resectable disease, and the

5-year survival rate for this locally advanced/metastatic population is abysmal (<25%).

Sadly, our ability to systemically treat metastatic soft tissue sarcoma chemotherapy remains a case of trial-and-error. Currently, the National Cancer Center Network guidelines list nine acceptable single agents and six different combination regimens.

Anthracyclines are considered to be the first line of therapy with no clear evidence of survival benefit(13). The addition of ifosfamide to anthracyclines had led to higher response rate in randomized clinical trials(14, 15). Even though there is limited evidence of efficacy, oncologists gravitate towards this therapy for individuals who are in good health and have advance or metastatic disease(16). More recently, there has been more interest in histology-driven chemotherapy(17-19). For example, eribulin was recently approved for treatment for patients with metastatic liposarcoma and angiosarcoma (often radiation induced) taxanes or gemcitabine with or without docetaxel are alternative options to anthracyclines(20, 21). Other examples are dermatofibrosarcoma protuberans that can be treated with imatinib(22) and alveolar soft part sarcoma, which can be treated with cediranib or sunitinib(23, 24). For patients who progress on initial therapy, there are several second line treatment possibilities. For example, trabectedin is approved for second line therapy and has been effective for leiomyosarcomas, liposarcomas and myxoid sarcomas(7, 25-27). Leiomyosarcomas and undifferentiated pleomorphic sarcoma appear to also respond to gemcitabine with docetaxel(7, 28). Further, pazopanib was also shown to have a clinical benefit is specific tumor types(29). The data supporting the use of these drugs will be discussed below.

The drugs that are commonly used have varied mechanisms of action. Ifosfamide is an intravenously administered alkylating agent and often requires activation in the liver. The active ingredients act by modifying and cross linking purine bases in DNA, ultimately inhibiting DNA, RNA and protein synthesis leading to apopotosis(30). Anthracyclines, intercalate between base pairs of DNA/RNA stands and inhibit RNA synthesis. They also block DNA transcription and replication by acting against topoisomerase II(31).

Gemcitabine is a pyrimide analogue, which replaces factors needed for nucleic acids leading to tumor growth and apoptosis. Docetaxel is a member of the taxane group, which interferes with the mitotic process by disrupting the function of microtubules that are necessary for cell division(32, 33). Dacarbizine, an alkylating agent, leads to cell death by adding an alkyl group to the DNA(32). Trabectidine does not have a clear mechanism of action and has been thought to generate a superoxide near the DNA resulting in DNA cleavage and apopotosis(34). Finally pazopanib is a small molecule that targets multiple tyrosine kinase receptors such as c-KIT, FGFR, PDGFR, and VEGFR which are involved in tumor growth and angiogenesis(35).

Oncologists have little idea whether an individual patient will respond until well after the start of chemotherapy, which exposes patients who will not benefit to the harms of doxorubicin-based chemotherapy including severe cardiac effects. Also, because of significant sarcoma heterogeneity there are neither established drug prediction algorithms nor treatment nomograms. In this analysis our primary aim is to better understand if p53 status (mutation, loss, wild type) can determine sensitivity to doxorubicin and other

4 agents which are commonly used to treat STS in patients who are treated at Wexner

Medical Center. Our secondary aim, is to determine if there are any common mutations, genetic abnormalities, among patients which can be correlated with progression free survival (PFS) or overall survival (OS). In order to study these two aims, we choose to evaluate patients who have been diagnosed with leiomyosarcoma. This specific sub-type was chosen as it is the most common histology of STS and based on the number of patients we have seen.

Leiomyosarcoma

Epidemiology

Leiomyosarcoma (LMS) originates in the smooth muscle tissues and is a malignant mesenchymal tumor that represents about 10-20% of all newly diagnosed STS(36).

Population based data on the incidence of LMS was investigated in the Surveillance,

Epidemiology and End Results (SEER) database and a total of 35, 359 STS patients were diagnosed during 2005-2009(36). Liposarcoma was the highest diagnosed followed by

LMS, which is the predominant sarcoma arising from large blood vessels. LMS is less commonly in the extremities (10-15% of limb sarcomas) but does have a preference for thigh(37). In addition, LMS of the uterus (uLMS) is the most common uterine sarcoma and has an incidence of 0.64 per 100,000 women(38).

The overall incidence of LMS increases with age and peaks at the seventh decade. In contrast, uLMS occurs from the third decade into old age and is most common in the perimenopausal group, fifth decade(39). The incidence of uLMS appears to increase with age, African American race and prolonged use of tamoxifen over 5 years. Most patients

5 with LMS in the retroperitoneum and inferior vena cava are female and there is a slight male predominance in noncutaneous soft tissue site and cutaneous leiomyosarcoma(36).

Etiology

There is not a clear causes or predisposing factor that have been identified for LMS.

Epstein-Barr virus (EBV), has been the only exogenous agent which has been studied in the setting of severe immunosuppression and has been associated with LMS among patients with acquired immunodeficiency (AIDS), and kidney, cardiac, and liver transplant. Further, most of the EBV associated LMS occur in children or young adult in sites which are not considered traditional for leiomyosarcoma (40). Other risk factors such as radiotherapy rarely cause LMS(41). Patients who have hereditary retinoblastoma have a cumulative risk of 13.1% of developing any STS as a secondary malignancy(42).

Finally, there is currently no evidence to suggest that leiomyoma can become LMS(36).

Histology

Leiomyosarcoma is a mesenchymal tumor that is composed of cells having distinct features of smooth muscle. It typically has a histologic pattern consisting of intersecting and sharply marginated fascicles of spindle cells with abundant eosinophilic cytoplasm and hyperchromatic nucli. Large leiomyosarcomas can contain tumor necrosis regions, focal pleomorphic can be common and sometimes show extensive pleomorphism which resembles undifferentiated soft tissue sarcoma. Most LMS are positive for alpha smooth muscle actin, desmin and h-caldesmon on immunohistochemistry(43).

Tumor Biology

From a molecular genetics viewpoint, soft tissue sarcomas can broadly divided into two categories 1) simple karyotypes that frequently have fusion or mutation of specific genes or 2) complex karyotypic abnormality which numerous chromosomal changes, non- recurring translocations and deletions; majority of STS is composed of the latter(44).

With the use of standard karyotyping and fluorescence in situ hybridization (FISH), it has been identified that cytogenetic and molecular changes in LMS are complex and there are no consistent or recurrent aberrations shown at the chromosomal level(45). Furthermore,

DNA copy number changes have also shown to have a complex landscape in LMS and the extent of gains/losses and cytogenetic change can be correlated with tumor progression as well as survival. The most consistent chromosomal changes that have been detected across several studies are losses in 10q11 to 21.2 and 13q14.3 to q21.1 and gains in 17p11 to p12. The regions which are deleted in 10q and 13q harbor important tumor suppressor genes: 1. RB1 and 2) PTEN(36).

Furthermore, the genetic mechanisms, poorly understood, also frequently include disturbances in key cell cycle genes. For example, the 13q specifically targets RB1. The retinoblastoma protein encoded by the RB1 gene, is a key regulator of proliferation, development and differentiation of different cell types. Though the exact mechanisms of sarcomas with complex karyotypic defects are poorly understood, alterations typically

7 include disturbances in cell-cycle genes. The RB1 gene can cause lack of regulation at the cell cycle level specifically at the G1-S checkpoint and lead to uncontrolled cell division. Analysis of LMS patients, has shown that components of the RB1-cyclin D1 pathway (RB1, CDKN2A, CCND1, CCND3) can be altered in 90% of patients; thus indicating, that this could be a key driver in LMS(46, 47).

Gene expression profile studies using expression arrays have identified three different reproducible molecular subtypes (Group I, II, III) that are distributed similarly between

LMS from gynecologic and non-gynecologic origin(48). Group I has approximately 25% of all LMS and is highly enriched for genes which are related to muscle contraction, has conventional LMS histologic subtype and has showed improved survival compared to leiomyosarcomas of groups II and III. This is consistent with previously published microarray data of pooled sarcoma subtypes that LMS can cluster with undifferentiated pleomorphic sarcomas and liposarcomas(36). Furthermore, molecular profiling studies have also identified new targets such as Aurora-A and Aurora-B kinases that are consistently overexpressed in uLMS. In-vitro and in-vivo targeting of Aurora-A has induced cell cycle arrest and apoptosis.

Several pathways have also been evaluated in leiomyosarcoma and phosphatidylinositol-

3-kinase (PI3K)/AKT pathway activation has been consistently shown(49). Deletion of chromosome 10q targets PTEN gene loss and leads to hyperactive of the PI3K/AKT pathway which is a common finding in LMS. Mice with inactivation of PET in smooth

8 muscle, rapidly develop abdominal leiomyosarcoma with activation of mammalian target of rapamycin (mTOR).

The p53 signaling pathway can be activated in response to various stress signals, leading to a transcriptional process ultimately leading to tumor progression. Loss of function of p53 either by mutations or other alternations in the pathway has been a common feature in many malignancies(50). More than 75% of mutations cause the loss of function of wild type p53 and may exert a dominant negative regulation. Interestingly, wild type p53 and mutant p53 both have oncogenic potential but their functions are entirely different (50).

Abnormalities in p53 in LMS have been known since 1998 and more recently been described in a study by Yang et al(51). An initial study done by Konomoto et al. showed that there was TP53 mutation in 8 of 15 superficial extrauterine LMS and 8/22 deep type extra-uterine LMS. Another study done by Perot et al. showed that the frequency of TP53 mutations was in approximately 42% of patients. The more recent study by Yang et al. showed that TP53 mutations were only seen in female patients and majority originated from uterus or retroperitoneum (51). The authors findings support the recent hypothesis that uterine and retroperitoneal LMS have similar histogeneisis.

ATRX is a protein coding gene which is involved in transcriptional regulation and chromatin remodeling. This facilitates DNA replication and is required for efficient replication of the genome. Recently, it was shown that 53-62% of LMS use alternative

9 telomere lengthening (ALT) as their telomere maintaining mechanism. ATRX and

DAXX form a dimer to the telomeres and plays a critical role in telomere stability (52-

54). Thus dysfunction of the dimer leads to telomere instability, ultimately alterative lengthening of telomeres. Previous studies have shown that ATRX mutations are highly correlated with loss of ATRX expression and the ALT phenotype in pancreatic neuroendocrine tumors and gliomas. A recent study evaluating ATRX mutations in LMS showed the ALT phenotypes. The patients with ATRX mutations with ALT phenotype had poorly differentiated LMS, presence of tumor necrosis, and had worse OS compared to ATRX wild type LMS. It has been well described in the literature, that ALT phenotype is associated with aggressive histologic features and poor clinical outcome(51).

Treatment

The mainstay of treatment of localized LMS is surgery and post-operative radiation in early disease has not been shown to have any beneficial outcomes. The standard surgical procedure involves a complete wide excision with wide negative surgical margins (R0) which offers the best chance of cure with or without adjuvant therapy. An R0 resection in retroperitoneal LMS is often difficult due to the large tumor and anatomic location, thus, clinically many of these tumors are grossly complete resection but have a positive margin(55). This is important to recognize as the ability to have a complete resection at the time of initial presentation is one of the most important prognostic factor for survival

(56). The role of adjuvant chemotherapy in early stage is also unclear and currently there are ongoing trials evaluating this.

Doxorubicin

For patients with metastatic disease LMS, surgery is generally considered as cytoreduction improved median progression free survival to 14.2 months compared to 6.8 months in patient with residual disease. Typically outcomes tend to be better for patients who have a prolonged time to recurrence (>12 months) as well as those with recurrence amenable to resection. For patients who are not surgical candidates, doxorubicin as single agent or in combination with ifosfamide could be considered as first line therapy.

Doxorubicin as single agent has a varying response rate of 10-25% in STS and LMS tends to be less responsive than other sarcoma subtypes (16%-19% in uLMS and a median overall survival (OS) of 7-12 months) (36). A review of 2185 patients with sarcoma and treated with doxorubicin showed that LMS had a nonsignificant lower response rate of 11% compared to other sarcoma subtypes (57). A more recent study, evaluated 488 metastatic patients with STS, 85% received doxorubicin for first line treatment and concluded that synovial sarcoma and liposarcoma were independent favorable predictive factors for response and survival compared to leiomyosarcomas (58).

Various doxorubicin combination regimens have been evaluated to improve response rates and overall survival, and higher response rates, approximately 45%, have been consistently shown in randomized control trials and pooled analyses with combination regimens compared to single agent doxorubicin(59). It is not known if these combination regimens have a shown a statistically significant advantage in overall survival and more

11 toxic effects were observed. If tumor shrinkage is critical, combination therapy can be justified. In addition, other anthracyclines such as liposomal doxorubicin or epirubicin can be used to minimize secondary effects. For example epirubicin is less cardiotoxic and has outcomes which is similar to doxorubicin. Liposomal doxorubicin has been toxicity profile and its efficacy compared to doxorubicin is still unknown(36).

Ifosfamide

Single agent ifosfamide has similar antitumor activity as doxorubicin in STS but has a worse toxicity profile which is a common reason why it is used as a second line regimen.

For patients who failed doxorubicin, treatment with ifosfamide achieves a response rate of 25%(59). Sleijifer et al. conducted a retrospective analysis on prognostic and predictive factors for outcome in first line ifosfamide and ifosfamide containing regimens in 1337 patients with advanced or metastatic STS (60). The authors found the median

PFS was 19 week and OS was 54 weeks. There was a non-significant trend for a lower response rate and lower median PFS in patients with LMS compared to synovial sarcomas or liposarcomas.

Gemcitabine and gemcitabine combination regimens

Gemcitabine has been evaluated in several phase II studies and has been shown to have a response rate of approximately 10% in soft tissue sarcoma patients who were previously treated. These studies included patients with leiomyosarcoma. (61-66)

Other agents which have been evaluated for therapy in soft tissue sarcoma and none as single agents achieved a response rate which was greater than 20%. Docetaxel and vinorelbine as single agents have almost no response(67, 68). Furthermore, dacarbazine and temozolomide have modest antitumor effect and data from phase II clinical trials showed a response rate of 15%(69). When gemcitabine is adding as a combination to any of the aforementioned drugs, there seems to be synergistic activity. Moreover, the combination of gemcitabine/docetaxel doublet has been shown to be highly active in patients with unresectable LMS and in those who have progressed on prior doxorubicin therapy(36).

A phase II study initially reported the activity of gemcitabine plus docetaxel in which patients with advanced LMS of uterine or other were recruited. Complete response was observed in 3 patients, partial response in 15, with an overall response rate of 53%(70).

Among the 16 patients who were previously treated with doxorubicin with or without ifosfamide, responses were seen in 50% of patients. Among the patients with non-uterine

LMS, partial responses were observed in 2 patients (40%), stable disease in 2 and disease progression in 1.

Two multicenter phase II randomized clinical trials showed that the combination of gemcitabine plus docetaxel produced synergistic effects. The SARC002 study showed that the combination was associated with higher response rate (16% vs 8%), median PFS

(6.2 months vs 3) and OS (17.9 months vs 11.5) compared to gemcitabine alone. This

13 study included patients with any STS and received a median of 1 line of therapy. In contrast, a French study confirmed the activity in uLMS but not in nonuterine origin. The

French study included only LMS patients after progressing on first line systemic therapy(28, 71).

Furthermore, Garcia del Muro et al. evaluated gemcitabine plus dacarbazine in a phase II randomized clinical trial(32). The combination was superior to single agent dacarbazine and the LMS subtype benefited significantly. Finally the combination of gemcitabine plus vinorelbine also achieved great benefit in LMS patients though not as impressive as gemcitabine with dacarbazine(72).

Trabectedin

Trabectedin is a unique chemotherapy agent which forms covalent bonds with the DNA minor groove, distorting DNA and blocking transcription. Several phase II trials testing trabectedin as single agent and as second line treatment in heavily treated metastatic or unresectable STS and leiomyosarcomas of any origin have been found to have a particular sensitive histology(25, 27, 73, 74). Data from 431 leiomyosarcomas of any origin showed a response rate of 7.5% compared to 5.9% for other sarcomas grouped together. Similarly LMS had a higher clinical benefit, median OS compared to non-LMS

(54%, 16.2 months in LMS and 38% and 8.4 months in non-LMS). A phase II trial single arm clinical trial in chemotherapy naïve patients found a higher response rate of 17.1% in patients with sarcoma. The median PFS was 16.5 months and 2/15 patients with LMS had

14 a partial response(75). This data led to the randomized, multi-center, phase III clinical trial to evaluate the efficacy of trabectedin vs standard doxorubicin as first line treatment.

Targeted Therapies

Antiangiogenic inhibitors

Traditionally the use of antiangiogenic inhibitors had been shown to have modest activity in STS especially in terms of objective response rate. When bevacizumab was added to doxorubicin, it not improve response rates compared to single agent therapy and increased cardiotoxicity(76). Similarly, Sunitinib and sorafenib, also showed small benefit in sarcoma but not in LMS(77, 78).

Pazopanib, a multi-target kinase inhibitor which targets vascular endothelial growth factor (VEGF), is the only approved targeted therapy for metastatic STS for patients who have progressed on doxorubicin based therapy. This approval was based on the phase III

PALETTE study which showed that pazopanib significantly increased median PFS to 4.6 months compared to placebo. There was no difference observed in OS(79).

mTOR inhibitors

Though there is strong rational for the use of mTOR inhibitors in LMS, preclinical models evaluating temsirolimus and ridaforolimus failed to show any significant benefit in STS(49). A phase II study of ridaforolimus in 216 patients with STS showed a low

15 response rate (1.9%) but had a clinical benefit of prolonged disease stabilization(80).

However a placebo controlled, phase III trial, comparing ridaforolimus with placebo reported a statistically significant but unlikely relevant PFS in those who received ridaforolimus. Temsirolimus also tested in a phase II study was shown to have poor activity(81).

Uterine Leiomyosarcoma

Uterine sarcomas are rare and constitute approximately 3% of all uterine malignancies of which leiomyosarcoma is the most common(82). Uterine LMS is associated with a poor outcome because rate of local and distant failure are high (45-80%) and median OS is 2 years in metastatic disease(83).

Currently, there are no distinctive or pathognomic features on imaging which would make the diagnosis, thus, histologic examination is necessary. Diagnosis must include two of the three criteria: 1) diffuse nuclear atypia, 2) increased mitotic rate and 3) coagulative necrosis(84).

Standard surgical management for non-metastatic disease is total abdominal hysterectomy. To date no adjuvant radiation or chemotherapy has been shown to have a survival benefit; thus, this not recommended. In advanced or recurrent disease, intent of treatment is palliative. Current treatment options include doxorubicin or ifosfamide, which had a response rate of approx. 20%(85, 86). For patients with metastatic disease uLMS, surgery is generally considered as cytoreduction improved median progression

16 free survival to 14.2 months compared to 6.8 months in patient with residual disease.

Typically outcomes tend to be better for patients who have a prolonged time to recurrence (>12 months) as well as those with recurrence amenable to resection. For patients who are not surgical candidates, doxorubicin as single agent or in combination with ifosfamide could be considered as first line therapy. Doxorubicin as single agent has a response rate of 16%-19% and a median overall survival (OS) of 7-12 months(87, 88).

Similarly, single agent liposomal doxorubicin and single agent ifosfamide have a response rate of 16%-17% in patients with recurrent or advanced uLMS(85, 89). The combination of doxorubicin with ifosfamide had the highest overall response and greatest progression free survival; however, there was no difference in OS. Furthermore, gemcitabine and docetaxel have also been shown to have promising results as first or second line therapy for patients with advanced LMS. The response rates ranged from

27%-36% with a median PFS of 4.4-5.6 months and OS of 14.7 to 17.9 months. A phase

III trial compared gemcitabine and docetaxel versus doxorubicin as first therapy for metastatic STS and found no difference between OS and PFS(90). For patients with poor performance status, single agent gemcitabine could be considered; however response rates are worse compared to the combination. Trabectedin is another one that can be used in patients with advanced disease. In a phase II trial, the response rate of trabectedin was found to be 10% with PFS of 5.8 months and OS of 26 months(91). When combined with doxorubicin as front line therapy, the partial response rate increased to 59.6%, disease control rate of 87.2%, median PFS of 8.2 months and OS was 20.2 months(92).

Dacarbazine and temozolomide have also been evaluated in STS however they have an

17 inferior PFS and OS when compared to trabectedin (32, 69). More recently, a randomized phase II study evaluated patients with advanced or metastatic STS with doxorubicin plus olaratumab, a monoclonal antibody against platelet-derived growth factor alpha. This combination led to significantly greater median OS, 26.5 months, compared to doxorubicin plus placebo (14.7 months), showed favorable objective response and PFS

(93). More recently, study by Wang et al. evaluated patients with advanced or metastatic

LMS who were treated with molecular targeted therapy. The authors found an improvement in median PFS and OS (5.8 months and 8.7 months respectively) compared to those who were not treated with molecular targeted therapy suggesting that gene alteration therapy can lead to improved benefit(94).

Chapter 2: Methods

Eligibility Criteria

All patients had to be older than 18 years of age with a pathologically confirmed diagnosis of leiomyosarcoma and had to have molecular testing with FoundationOne. All patients received systemic treatment with currently accepted method of treatment for

LMS (surgery, radiation, chemotherapy, or targeted agent). In addition, all patients had an Eastern Cooperative Group (ECOG) Performance Status (PS) of 0 or 1 in this study.

Study Design

This is a retrospective analysis of patients who received treatment for the diagnosis of sarcoma from January 1, 2012 to December 31, 2016. All patients were required to have molecular testing with Foundation Medicine. The Ohio State University Institutional

Review Board approved this study. All patients were evaluable for toxicity, progression free survival and overall survival. For our evaluation of patients, split our patients as two cohorts: 1) all patients regardless of stage of disease and 2) patients with metastatic disease at diagnosis. Date of tumor progression was verified with computed tomography

(CT) imaging and date of imaging was used as the true date of progression. If CT scans were not available at time of clinical progression, the closest imaging was used for comparison. All patients who were included in the analysis must have had

FoundationOne testing completed with results available.

Endpoint

Our hypothesis is that mutations or loss of p53 function can lead to resistance in doxorubicin based therapy in soft tissue sarcoma. The primary aim of this study is to identify if p53 status can determine sensitivity to doxorubicin based on the duration of response either progression free survival or overall survival. The secondary aim is to identify if there are any common mutations among the specific subset of patients that correlate with progression free survival or overall survival.

We analyzed the results of our data in three different ways. The answer the first aim of the project, we evaluated the type of p53 mutations which patients can have and evaluated the progression free survival a specific therapy such as doxorubicin, pazopanib or gemcitabine + docetaxel. To evaluate the second aim, we generated a heatmap using the programming language R. We then dived the dendrogram that was created into 6 different clusters based on the groups of genes that belonged to the same pathway. We performed two different analyses on the clusters, one which includes all of the patients and the second includes only patients who had metastatic disease. We used Kaplan Meier curves to estimate survival and to determine if there are any differences among the groups. Since uLMS is the most common subtype of LMS, we created a similar dendrogram using R for those patients and created 5 different clusters. Similar to before, we compared each of the clusters to see if there is a difference between any of them. The third way in which we evaluated the data was to see if there were any same recurrent

20 mutations either significant or non-significant for at least 3 patients. We arbitrarily chose this as a cut off point given the ambiguity in the literature.

Statistical Analysis

Descriptive statistics (mean, standard deviation, median, range, proportion, 95% confidence interval) will be used to summarize the data collected for the primary and all secondary endpoints. Pearson Chi-square test will be used for the association between a categorical factor and the response (yes or no). Simple logistic regression model will be used to evaluate the univariate correlation between the continuous variables and response to doxorobicin. Multivariable logistic regression models may be limited due to small number of responders but will be considered, if possible, to take into account several covariates showing significant univariate correlations with response. Kaplan-Meier curves will be estimated and displayed for PFS and OS and will be evaluated for the correlation to doxorubicin therapy. Fisher exact test was performed to compare the different groups in the clusters which were formed using R.

All statistical analyses were performed using Graphpad Prism by log-rank (Mantel Cox) test or statistical package in R. Heatmaps were generated using the binary distance algorithm and Euclidean distance algorithm in R software package. After using the

Euclidean distance algorithm, we used the complete-linkage clustering to generate the heatmap which is the default algorithm in R.

A heat map is a graphical representation of data where the individual values contained in a matrix are represented as colors. Dendrograms are used to describe the similarity between clusters and/or observations. There are a variety of heat map packages in R. The

.heatmap() with parameters of euclidean distance and complete clustering was used for the all patient cohort. The heat map with just uterine patients was done using heatmap.2() which has binary distance and complete clustering. Both function generate the same output, however, the second function has a plot which is slightly easier to read.

FoundationOne

Foundation Medicine, Inc. is a company located in Boston, MA that provides a validated comprehensive genomic profile, FoundationOne, and evaluates the coding sequence of

315 cancer related genes that can be altered in solid tumors. This testing classifies genomic alterations, base pair substitutions, insertions, deletions, copy number variations and gene rearrangements that are clinically relevant. Once an alteration is identified, the specific alteration is evaluated in the scientific literature and if it is not adequately characterized, the alteration will be listed as a variant of unknown significance.

FoundationOne applies next generation sequencing to identify all types of alterations that can be drivers in solid tumor malignancies with immense accuracy. A tumor sample size of ≥ 40µm tissue, of which 20% is of malignant origin, on 8 to 10 unstained pathology slides, a formalin-fixed paraffin-embedded (FFPE) block, or needle biopsy is needed for

22 testing to be done. The specificity to identify base substitutions, insertions, copy number alterations and rearrangements is > 99%.

The method by which this testing is done, DNA is extracted from unstained FFPE specimens using the Promega Maxwell 16 Tissue LEV DNA kit (Promega, Madison, WI, http://www.promega.com) and quantified using an Invitrogen PicoGreen fluorescence assay (Thermo Fisher Scientific, Wal- tham, MA, http://www.thermofisher.com). Cancer- related alterations were defined as those that are known sites of somatic mutation, truncations or homozygous deletions of known tumor suppressor genes, and amplifications of oncogenes and fusions of genes, which are rearranged in solid tumors.

Chapter 3: Results

Patient Characteristics

A total of 61 patients were diagnosed with leiomyosarcoma and treated at Wexner

Medical Center. Fifty-eight patients had molecular data from FoundationOne available and were included in the study. The median age of diagnosis was 58 with 10 males and

48 females. Of the 58 patients, 43 developed metastatic disease and 28 presented with metastatic disease at time of diagnosis. A total, both metastatic and local disease, of 15 patients were deceased at the time of analysis, suggesting there needs to be another evaluation after a period of time. An analysis of the 58 patients showed that at least one alteration, either significant or variant of unknown significance, is seen in all patients

(Table 1).

In our patient population, LMS site of origin was mostly commonly from uterus (48%), retroperitoneum (16%), extremity (13%), gastrointestinal (7%), cutaneous (5%) and other

(composed of many different sites) (11%). The most common alterations, which were seen in all patients, was TP53 (68.9%), RB1 (51.7%), ATRX (24%), BRCA 1/2 (17.2%),

CDK 4/6 (15.5%), PTEN (13.7%), and TSC2 (10.3%). When we evaluated only the patients who presented with metastatic disease, the most common alterations were TP53

(60.7%), RB1 (53.4%) and ATRX (21%). There was no difference that was observed between all patients regardless of stage compared to the metastatic patients. Fifty one percent of patients did have an alteration in the PI3K/AKT pathway either through loss of

24 negative regulators (PTEN, TSC1, STK11, NF2) or by gain of function by positive regulators (RICTOR, AKT2, AKT3, PIK3CA). Further, 16% had a genomic alteration in one of more genes, which are participating in the CDK 4/6 pathway (CDN2A/B,

CDKN2C, CDK4, CDK6, CCND1, CCND2, and CCND3). Mutational analysis looking at the number of mutations, both significant and variance of unknown significance

(VUS), indicate the median number of significant mutations among all patients is 3, median number of VUS is 9 and median for total number of mutations is 12 (Figure 1).

Patient with metastatic disease at diagnosis had a median number of mutations of 12 which is similar to those who did not have metastatic disease. The median number of significant mutations in those with metastatic disease was 3 compared to 4 in those who did not have metastatic disease (Figure 2).

Efficacy Analysis

TP53

TP 53 alterations, either loss or mutation, were identified in 68.9% of all patients and

60.7% of patients with metastatic disease. The effect of p53 wild type (WT), mutant

(MT) and loss was evaluated in all patients, metastatic patients, and those patients who received doxorubicin, pazopanib and gemcitabine + docetaxel.

For the whole cohort, there were 40 patients who had an alteration in p53 either WT, MT, or loss. Twenty-four patients had p53 mutations, 14 patients with loss and 19 patients were WT. In both the MT and loss, 50% of patients had tumors, which was originating in the uterus or retroperitoneum. We performed a Kaplan Meier analysis, which suggests

25 that patients who were WT had better OS compared to the MT or loss group. A separate analysis of evaluating patients who presented with metastatic disease showed similar results (Figure 3). In this analysis, there were a total of 28 patients, 11 had p53 mutations,

6 had loss of p53 and the remainder was WT. TP53 seems to have a prognostic value more than a predictive outcome as those who are p53 WT are have much longer OS compared to those who are MT or loss.

P53 + doxorubicin

There were a total of 33 patients who received doxorubicin in our cohort either as neoadjuvant therapy, adjuvant therapy or in advanced disease. Within this group 7 patients had a loss of p53, 14 patients had a MT p53 and the remainder were WT. Progress free survival was calculated using Kaplan Meier curves. When we compare the MT, loss and

WT p53, it seems like the patients who have a WT p53 are more like to do better when treated with doxorubicin. Though our data is not significant, likely due to sample size and maturation of data, the trend is appearing this way. We also combined the loss and mutated patients to see if this group versus the WT patients to see if this makes any difference. It is likely that we are not seeing a significant p-value due to the low sample size.

We then evaluated only the patients who presented with metastatic disease to see if the p53 status has any effects on PFS with doxorubicin. Of the 28 metastatic patients, 18 patients were treated with doxorubicin. There were 3 patients with p53 loss, 8 with MT and 7 WT p53. Similar to prior, we used Kaplan Meier curve to calculate progression free

26 survival and found that patients who have loss p53 possibly respond better to doxorubicin. Though our data is not significant, this is a trend that we are noticing. When we combine the MT and WT cohorts for the metastatic patients, we find this holds true and the data is still not significant due to low number of patients (Figure 4).

Evaluation of patients who did not present with metastatic disease also shows that patients who are WT had a longer PFS compared to those who are MT or have loss of p53. The total number of patients in this group was 15 (5 patients with WT, 6 with MT, 4 with loss). The p-value was not significant however the Kaplan Meier trended is clear

(Figure 5). The lack of statistical significance is possibly due to the low number of patients.

It seems that p53 is a predictor of response in patients with metastatic disease as those who were treated with doxorubicin could possibly have better PFS compared to the MT or WT group. Evaluation of patients who did not develop metastatic disease and were treated with doxorubicin is consistent that those who are p53 WT have better PFS compared to those who had a MT or loss also suggesting that this is a prognostic factor.

TP53 + pazopanib

In our cohort of 40 patients with alterations in p53, 22 patients have received pazopanib when we evaluate all patients regardless of stage at initial diagnosis. Though not statistically significant, the data suggests, that those who are p53 WT may do better when not controlled for stage. There were 11 patients who received pazopanib and had

27 metastatic disease at diagnosis. Evaluating only these patients, it is clear that the curves separate and the p53 MT patients tend to perform better. Again, this is a trend we are noticing due to the low sample size (Figure 6). TP53 mutation could be a predictive biomarker for those who should be treated with pazopanib.

TP53 + gemcitabine + docetaxel

We evaluated 28 patients who received gemcitabine + docetaxel regardless of stage of diagnosis and had an alteration in p53. Patients who have a WT p53 possibly perform better than those with MT or loss. There were 19 patients who presented with metastatic disease and received gemcitabine + docetaxel. Among this group of patients, those who are p53 MT probably perform better; however, there have not been enough patient events and the total number of patients is low to make a definitive conclusion (Figure 7). This suggests that status of p53 probably does not have a predictive or prognostic effect on treatment with gemcitabine with docetaxel.

BRCA

We identified a total of 10 patients who had an alteration in BRCA 1, BRCA 2, or BRCA

2 loss. Of the 10 patients, 3 patients had BRCA 2 loss, 3 patients had BRCA 1 mutation and 6 patients had BRCA 2 mutations (Table 2). There is one patient who had a BRCA 1 mutation with BRCA 2 loss and one patient BRCA 1 and 2 mutations. None of the patients who had BRCA alterations were from cluster 5. Six of the 10 patients developed metastatic disease and site of origin was varied among all of the patients with the most

28 common being uterine in 4 patients. Further, 7/10 patients had a family history that was significant for other BRCA tumors such as breast, uterine, prostate and pancreas cancer.

The history was not complete for the other 3 patients (Table 3).

Of the six patients with BRCA2 mutations, three had a concomitant MED12, which was the same and a p53 mutation. Further, 5/6 patients had a mutated p53, were female and

4/6 developed metastatic disease. Four out of six patients had family history significant for breast cancer. The most common BRCA 2 mutation observed was K3326 in 4 patients. This is reported as a variance of unknown significance on Foundation Medicine.

Three out of the four patients with this mutation were female and had family history that was significant for breast cancer either in first or second-degree relative. The one male patient had no family history which was significant for malignancy (Table 4).

The three patients who had BRCA 2 loss were all female and the original site of tumor was from uterus. Two of the three patients had an RB1 mutation and MEN1. Of the three patients who had BRCA 1, 2 developed metastatic disease and only one had family history significant for breast cancer.

Comparing patients with BRCA 1/2 abnormalities (mutations, loss) to those without among all patients, it seems like those with an alteration are likely perform better suggesting this could be prognostic (Figure 8). On comparing only metastatic those with

BRCA to those without, suggests that this is still probably a prognostic biomarker (Figure

8); however, there are a low number of patients to definitively make this conclusion.

The median time of response for patients who received trabectedin was longer for those who had BRCA alterations compared those who did not (5 vs 2 months) which is consistent with what is seen in the literature. There was one patient who had a longer PFS compared to the rest in those who did not have a BRCA alteration at 7 months when treated with trabectedin. It is not clear as to why this patient had a remarkable response.

We currently have 2 patients who are therapy with olaparib and the median response time was 4 months. One patient has been on therapy for five months and still has stable disease based on RECIST criteria. Currently, we are in the process of obtaining olaparib for two more patients who have BRCA alterations.

Cluster Analysis

There were two different heat maps which were generated 1) includes all patients with leiomyosarcoma and 2) only those with uLMS. We generated a separate heat map for uLMS as this is the most common site of origin for LMS and wanted to evaluate if there is a molecular difference among (Figure 9).

Patients in this diagram were divided into different clusters based on similarities in the pathway in which the genetic alteration. In the all patient LMS heat map, patients were divided into 6 cohorts. Cluster 1 included patients who had alterations in p53, RB1, and the PI3K/ATK pathway, cluster 2 included patients who had alterations in the CDK pathway, cluster 3 patients did not have any specific pathway alteration and all patients had non-specific mutations, cluster 4 had alterations in RB1, cluster 5 patients had alterations in p53, RB1 and ATRX and cluster 6 had alterations in p53 and RB1. On

30 comparison of each individual cluster to one another, it seems cluster 3 had the longest

OS. Further when we separate out only metastatic patients, clusters 1 and 4 seem like they perform the worst and cluster 3 has the longest OS. Compared to the remainder of the clusters, those in cluster 3, had non-specific mutations.

In the uLMS heatmap, patients were divided into 5 cohorts. Cluster 1 had patients with alterations in p53, RB1, and the PI3K/ATK pathway, cluster 2 includes patients with p53 and RB1 alterations, cluster 3 is only RB1 alterations, cluster 4 is p53 and PI3K/ATK and cluster 5 includes all patients with non-specific alterations.

Clinical implications of clusters- All patient cohort

We reviewed the clinical characteristics of all the patients in our dataset and compared it to TCGA as well as GENIE. The TCGA had a total of 98 patients who had leiomyosarcoma and 28 patients who had uLMS. GENIE had a total of 198 patients of which 86 had uLMS. We compared our cohort to both of these datasets (Table 5).

Cluster 1

There were a total of 12 patients in cluster one. All patients were female and had high- grade leiomyosarcoma. There were no other similarities among this group of patients.

Comparing this cluster 1 to the remainder of the patients does not show any difference between the two groups. However, comparison of cluster 1 to the remainder of the group, only in metastatic patients, indicates that this group possibly does worse.

Comparison of our data to TCGA indicates does not show any similarities. Patients in our 31 cohort did worse compared to those who were in cluster 1 in the TCGA. Both data groups had a low number of patients so this should be interpreted with caution. Further, all patients in our data were female which was not the same in the TCGA.

Cluster 2

In cluster 2 there were a total of 3 patients in this pathway. All these patients had LMS from uterus and 66% had metastatic disease at diagnosis. Interesting, none of these patients had alterations in p53 or RB1 in the presence of a significant CDK alteration.

Other alterations that were similar among this group of patients were MLL3, GRIN2A,

NCOR2, FAF1 and STAT6. At this time, the significance of these alterations is in relation to CDK, is not known. Comparison of patients in cluster 2 versus all patients as well as those with metastatic disease only, indicates that patients in this cluster could potentially do better.

In the TCGA dataset, patients who were in cluster 2 had a significantly worse outcome (p

=0.039) compared to the remainder of the group. In contrast, patients in cluster 2 of our data did better. The number of patients is our cohort is small so it is difficult to make this comparison accurately. In addition, patients in our cohort with this alternation were treated with targeted therapy. Should these patients not have been treated with targeted therapy, the responses seen could possibly mirror the TCGA (Figure 10).

Cluster 3

There were a total of 17 patients (largest cluster) in this group. Six of the 17 patients were

32 male. Majority of the male patients from the entire cohort were in cluster 3. There were 3 patients with BRCA2, 4 with IRS2, 4 with TSC2 and 4 with PCLO. The specific subtypes of mutations were different among the patients who had the alterations. Fisher Exact test was significant for the number of males who were in this group (p < 0.026). There is not a clear understanding of why the majority of the male patients fell into this category.

Furthermore, in the TCGA dataset, there were a total of 33 male patients and 23 were included in this category (approx. 69.9%). The percentage of men in this category is significantly higher than the remainder in both datasets suggesting that the biology of male patients is significantly different than female.

Evaluating patients in cluster 3 compared to the rest shows that these individuals possibly do not perform any differently than the remainder of the group. This likely the same when comparing metastatic patients in cluster 3. Similarly, in the TCGA dataset, patients who had non-specific alterations did better compared to those who did not (p=0.022)

(Figure 11).

Cluster 4

In this cluster there were a total of 7 patients and 5 were female. Majority of the patients had the primary site arising from the uterus and had metastatic disease at time of diagnosis. FLCN was the most commonly seen mutation that is a variance of unknown significance.

Patients in cluster 4 possibly have a worse OS compared to the remainder of the cohort

33 thought the exact reason is not known. This is possibly the same in patients who presented with metastatic disease. There are no similarities between clusters 4 in our group versus the TCGA. Given the low number of patients in this cluster, it is difficult to interpret the Kaplan-Meier curves.

Cluster 5

A total of 6 patients were included in this cluster and all were female. Half of the patients had primary tumor site in the uterus and presented with metastatic disease at time of diagnosis. There was no common variance of unknown significance mutations that were observed. Similarly, in the TCGA dataset there were a total of 3 patients in this cluster and all were also female.

Comparison of patients in cluster 5 to the remainder shows that these individuals possibly have worse outcome compared to the rest of the cohort. In patients diagnosed with metastatic disease, cluster 5 is difficult to interpret, as there were not enough events to make a conclusion.

Cluster 6

Twelve patients were included in this cluster and 11 were female. Nine of the twelve patients had high grade LMS and majority developed metastatic disease to the lung.

Approximately one third of the patients had metastatic disease at time of diagnosis.

Similarly, majority of the patients in the TCGA dataset were also female in this cluster.

Comparing patients in cluster 6 to all patients as well as the metastatic cohort only, it 34 does appear that these patients have any difference in outcome. Since there are a low number of patients, this data is not significant. However, in the TCGA dataset, patients who only had alterations in the p53 and RB1 pathway did significantly worse compared to the remainder of the cohort (p =0.0021) (Figure 12).

Clinical implications of clusters- uLMS patient cohort

Comparing patients with the uLMS subtype as primary site of origin to all other site in any stage of malignancy, those with uterine primary possibly have a better outcome. In those with metastatic disease, patients with primary uterine seem to do better (Figure 13).

Patients in the uLMS cohort were divided into 5 clusters based on the distribution seen on the heat map. There were a total of 27 patients who in this cluster that includes patients with any stage disease (Figure 14).

Cluster 1

In this cluster there were a total of 3 patients who had alterations in p53, RB1 and

PI3K/AKT. Patients in this cluster seem to do worse than rest; however is not for certain as there has not been enough patient events and there are only a few number of patients in cluster 1. In contrast, patients in TCGA cluster 1 could possibly have better outcomes compared to the rest (p =0.32). Since there is a low number of patients who fit this criteria it could be the reason as to why the data is not significant.

Cluster 2

There were a total of 8 patients in this category. All patients in this cluster have a p53 and 35

RB1 alteration. There were four patients who had alterations in ATRX. Patients in cluster

2 seem like they do worse compared to the remainder of the group. Similarly, those in cluster 2 in the TCGA also have a similar pattern in terms of outcomes. Given the low number of patients, the data is not significant in both datasets and needs to be validated in a larger cohort of patients (p=0.57). This is a trend which we are noticing in these two groups (Figure 15).

Cluster 3

A total of 2 patients were included in this cluster and both patients have RB1 alterations.

It is difficult to make any conclusions as there are not enough patients in cluster 3 to compare to the remainder of the patients.

Cluster 4

Four patients were seen in cluster 4. All patients in this had alterations in p53 along with

PI3K/AKT. Patients in cluster 4 seem like there is possibly a longer PFS in this cohort of patients compared to the rest. In contrast, patients in the TCGA did worse compared to the remainder of the group. There were only 4 patients who were seen in this group in the

TCGA which is exactly the same as our cohort. The discrepancy between the two groups highlights the heterogeneity which is seen in this disease.

Cluster 5

A total of 10 patients were seen in this cluster. There is no specific alteration that is similar among all of these patients. None of these patients have any alterations in p53, 36

PI3K/AKT and three patients had an alteration in RB1. Similar to cluster 4, patients in this cluster seem possibly have a longer PFS compared to the rest of the group. There were 10 patients in cluster 5 compared to the 17 patients in the rest of the cluster.

Similarly, patients who were in cluster 5 in the TCGA seems like they performed better, though not statistically significant (p=0.46). Better analysis could be done if there were a higher number of patients who can be compared in either dataset (Figure 16).

Chapter 4: Discussion

We present one of the largest cohorts of patients with leiomyosarcoma who had next generation sequencing from Foundation Medicine. Analysis of these patients shows that this disease is heterogeneous and suggests that a “one size fits all” therapy may not be a good option. Analysis of our data indicates that p53 functional status (mutations, loss,

WT) can be a prognostic biomarker for patients with LMS. Individuals with p53 WT, regardless of stage, are more likely to have a better outcome. Similarly, a study done by

Das et al. the authors evaluated 31 patients with STS and noticed a decreased survival in those with p53 MT compared to WT (95). Another study by Taubert et al. (96) showed that there were prognostic differences between the mutations types. The authors evaluated 145 patients with STS and found that those with frameshift mutations have a considerable worse outcome than patients without (96). They conclude that not only mutational status can be used, it is the specific subtype which can influence the outcome.

In sarcoma mouse models, it has been shown that mutations to p53 can have resistance to anthracyclines. Similarly, overexpression and/or mutations in p53 has been shown to have lack of response to cisplatin therapy in non-small cell lung cancer and poor prognosis in breast cancer (97-99). To our knowledge, this is the first study to evaluate drug response based on NGS in leiomyosarcoma.

The role of p53 in modulating chemosensitivity has been controversial. In colorectal cancer, p53 WT sensitizes colorectal carcinoma cell to chemotherapeutic agents such as 38

5-fluorauracil and topotecan (100); however, in other tumor types inactive p53 improves sensitivity (101). It is evident that p53 status and response to therapy is different based on tumor subtype. Usually, those with p53 WT expression pre-disposes cells to a more rapid death after DNA damage. Thus, mutations in p53 can lead to drug resistance. Pre-clinical study done by Zhan et al. (102) have shown that tumor cell transfection with p53 WT can cause tumor regression in 40% of mice treated with doxorubicin by downregulating multi-drug resistance protein-1 (MDR-1). In activation of p53 WT could occur as a result of anti-p53 antibodies. It has been shown that the presence of these antibodies is associated with decreased sensitivity to doxorubicin in esophageal cancer (103). We evaluated of all LMS patients with p53 alterations regardless of stage of disease, it seems that patients with p53 loss or WT had longer PFS survival when treated with doxorubicin compared to those who were WT. A separate analysis of only metastatic patients shows a similar finding, though as not a robust difference, due to the low number of patients.

Furthermore, patients who never developed metastatic disease and had p53 WT or loss, also had a longer PFS when treated with doxorubicin, either as neo-adjuvant or adjuvant therapy. Those with p53 WT had the best outcome. This is no surprising as an intact p53 would allow for apoptosis to occur with can be a synergistic effect with a doxorubicin as it is a topoisomerase II inhibitor and prevents G1 to S phase of the cell cycle. This is what we would have expected there would be a synergism between p53 WT and doxorubicin.

Pazopanib is a multi-tyrosine kinase inhibitor has been approved for therapy in sarcoma based on the phase III PALETTE study. Recently, it has been shown that patients who

39 have mutations in p53 respond better than those who are WT for p53. Pre-clinical studies have shown that WT p53 suppresses angiogenesis in leiomyosarcoma and synovial sarcoma cell lines. Thus, in patients who have mutant/loss of p53 there is an increase in angiogenesis. We observed that in patients who have advanced LMS and mutant p53, have a longer PFS compared to those who are WT or loss p53. This was recently shown by Koehler et al. and suggested that p53 mutation could potentially serve as a biomarker for of response (104). Our findings validate what was previously published and warrant further evaluation in a larger trial.

Comparison of our dataset to The Cancer Genome Atlas indicates that there is a difference in the most frequently altered genes which highlights the heterogeneity of this malignancy. The mostly commonly mutated genes in TCGA are p53 (51%), RB1(15.3%) and ATRX (13.3%). In our cohort, 51% of patients have an alteration in the

PI3K/AKT/mTOR pathway either through loss of negative regulators (PTEN, TSC1,

STK11, TSC2, NF2) or gain of function by positive regulators (RICTOR, AKT2, AKT3,

PIK3CA). We evaluated genes with variance of unknown significance that was not included in the TCGA. Furthermore, 16% had genomic alteration in one or more genes participating in the CDK4/6 pathway (CDKN2A/B, CDKN2C, CDK4, CDK6, CCND1,

CCND2, and CCND3). The COSMIC database shows that the frequency of commonly mutated genes in LMS is different than the TCGA. Neither of one these datasets differentiated LMS based on the original site of origin and did not include VUS which is likely the reason why there is a difference in the frequency of alterations which is

40 noticed. Given the significant variations, which are observed it is important that there is a collaborative effort, perhaps in a clinical trial, to determine the specific sub-types of

LMS.

Analysis of all patients with LMS, in our dataset, we were able to identify six different subtypes within LMS based on the molecular profiles. The most common pathways which were identified were PI3K/AKT, CDKN2A, RB1, p53 + RB1 and p53 + RB1 +

ATRX. Utilizing NGS, can allow identification of which specific cluster patients are more likely to resemble and therapy can be determined based on that cluster. For example, if a patient were to have CDKN2A alteration, therapy with palbociclib would be reasonable. Recently, Elvin et al. sequenced 279 patients with uLMS and found that

19% of uLMS patients have alterations in CDKN2A pathway. The authors describe a patient treated with palbocicib who had an extended clinical benefit from therapy. This can be the rational for a clinical trial for evaluating the CDK pathway in patients with uLMS. In the TCGA patients who had alterations in the CKD pathway did worse compared to the remainder of the patients. In contrast, we had three patients with alterations in CDK pathway, one of which is being treated with palbociclib and deriving clinical benefit. Though the total number of patients is small, it corroborates the idea certain individuals would get a benefit from targeted therapy and further validation needs to be done in a larger clinical trial.

Sixty percent of the male patients in our cohort, were grouped in cluster 3 based on the genomic profile. Review of this group of patients show that majority had p53 alteration or ATRX alteration. None of the patients had an RB1 alterations. We obtained the dataset from TCGA and GENIE and mapped the genetic profile of those patients to how our clusters were divided. We found that 69% of males in TCGA and 62% in GENIE were grouped into cluster 3. Comparison of cluster 3 to the remainder of the patients, seemed like there could be an improvement in OS however this was not significant due to the low patient numbers. Given that majority of the males are in this cluster and better improvement in survival, it is possible that LMS is a different disease entity for males vs female.

In the uLMS cohort, we divided patients into 5 different clusters based on the heatmap which was generated: 1) p53 + RB1 + PI3K/AKT, 2) p53 + RB1, 3) RB1, 4) p53 +

PI3K/AKT, and 5) non-specific alterations. Comparing each individual cluster to the rest, we found that cluster 4, 5 have better outcome compared to the remainder of the patients.

We hypothesized that RB1 could be a predictive biomarker for patients with uLMS.

Analysis of cluster 4,5 compared to the rest shows that patients who have WT RB1 had a better outcome (p=0.0477, Figure 17). We evaluated this in the TCGA, however we did not get a similar result which we believe is due to the small patient number. Better results could be obtained if the sample size were bigger. We could not evaluate this in the

GENIE dataset as the clinical information for survival is not available.

BRCA

The most common cause of hereditary breast and ovarian cancer is due to a germline mutation in BRCA 1 or BRCA2. Historically, the prevalence of BRCA1/BRCA2 mutations has been varied due to the method of testing e.g. sanger sequencing with or without duplication or replication testing. Large studies have indicated the genomic rearrangements account for 12-18% of all BRCA1/2 mutations therefore quantative PCR or microarray based techniques should be used in addition to sequencing(105). Patients with these mutations have a very high risk of breast or ovarian cancer by age 70, specifically 47-66% of those with BRCA 1 and 40-57% of those with BRCA 2(106).

Furthermore, women with BRCA mutations also have a higher chance of secondary malignancies such as male breast cancer, prostate cancer, adenocarcinoma of the pancreas, several gastrointestinal cancer including gall bladder, bile duct, colon and stomach, and melanoma(107). In soft tissue sarcoma, the incidence of BRCA mutations has not been consistently reported. One study evaluated molecular profiles of 1,023 leiomyosarcoma patients of which there was 625 uLMS. The most common alterations which were noted were TP 53(41%) and BRCA2 (6.3%) (94).

More recently, Elvin et al. evaluated 279 patients with advanced or metastatic uLMS and reported that the incidence of BRCA 2 was approximately 5% (108). There have been a couple of reports of a rare truncating BRCA2 variant (K3326X) which has been associated with a 2.5x increased risk of lung squamous cell carcinoma, upper digestive tract squamous cell carcinoma (oral cavity, larynx, and esophagus), and been associated with increased risk of developing breast and ovarian cancer independently of other

43 variants in BRCA2 (109). Furthermore, Martin el al. evaluated the biological significance of this deleterious stop codon in familial pancreatic cancer (110). The authors found that this polymorphism was significantly more prevalent in individuals with familial pancreatic cancer compared to the control group (5.6% vs 1.2%, OR 4.84, p<0.01) suggesting that this polymorphism contributes to pancreatic cancer risk. To our knowledge, there have been no reports of this rare mutation reported in soft tissue sarcoma or leiomyosarcoma.

Mazoyer et al. first described this polymorphism stop codon, K3326X, resulting from a

10204 A > T substitution while screening for germline BRCA2 mutations in familial breast cancer (111). The authors reported an allele frequency of < 1% in the control population and did not find there was increased risk of breast cancer which was associated with this polymorphism; thus, concluded this is a non-sense variant which does not increase risk of malignancy. Subsequently, Krainer et al. described this mutation in a cohort of 73 patients with early onset breast cancer and did not find this in matched controls (112). Since then, K3326X has been identified in several malignancies either alone or in combination with other deleterious mutations.

In our study, we had a total of ten patients who had alterations in BRCA 1 and BRCA 2 and four patients had had resulting the stop codon K3326X (Table 4). Clinical characteristics of these patients indicate that 75% of patients have family history significant for breast cancer in the first degree relative and a median age of 69. It is

44 possible that those with this alteration can develop malignancies later compared to the

BRCA alterations that we are already well aware of. BRCA testing in our patients was found as a somatic mutation, both significant and VUS, as it was seen on Foundation One reports. We do not germline mutational analysis to determine if there is an association.

We will be performing germline testing to determine if there is a true relationship between the two.

Chapter 5: Public Health Relevance

In 2015, there was a new precision medicine initiative, which stemmed from the enthusiasm that this would contribute to clinical medicine and advance public health.

Precision medicine, though focuses on individual care, the success of this is based on a population based approach as there is necessity to understand what interventions work for an individual and this data needs to be compared to a larger, more diverse group of individuals in a similar population.

With the use of next generation sequencing, several genetic abnormalities have been identified and now are used routinely in clinical medicine for identify those who are at risk of developing certain conditions. For example, mutations in BRCA. Initially, testing for these mutations was limited to women who had malignancy at a young age, substantial family history, bilateral breast cancer, or both breast and ovarian cancer (113).

More recently from population-based studies, it was show that patients who are BRCA 1 and BRCA 2 carriers from the general population, regardless of family history of malignancy, have very high risks of malignancy by age 80 (114). For patients who develop advanced or metastatic malignancies, testing from next generation sequencing is routinely done to identify any possibly therapy. Often there is identification of either mutations, amplification, translocation or deletions of a specific gene and the clinical significance is not known. An example of such is BRCA K3326X which is a rare truncating mutations of BRCA 2. Evaluation of this mutation in the general population 46 did not turn out to be an increased risk for any malignancy. However, there has been conflicting evidence. Several studies have associated BRCA K3326X with increased risk of lung cancer, ovarian and breast cancer, Ewings sarcoma, and familial pancreas cancer.

Though the data is not consistent, likely to due to tumor heterogeneity, analysis of tumor samples, number of patients, BRCA alterations in sarcoma have been seen about 5% of the time. In our cohort we identified 17% of patients with an abnormality in BRCA 1 or 2 and had a 7% of patients with BRCA K3326X alterations. Review of these patients characteristics shows significantly family history of BRCA associated malignancies which suggests that this alteration could be an increased risk for leiomyosarcoma. Since this is a somatic alteration, it is not known if there is a germline BRCA abnormality. We will be testing our patients to determine if there is a germline BRCA alteration so that patients can be appropriately screened and counseled.

Patients who present with advanced or metastatic disease generally receive chemotherapy and tumor analysis by next generation sequencing is done at time of progression to determine next line therapy. In STS or leiomyosarcoma, the response rate to “standard chemotherapy” is dismal and using NGS at time of diagnosis would be beneficial to identify which patients would likely derive benefit from targeted therapy, immunotherapy or chemotherapy. Identification of drug sensitivity to NGS, will help decrease the morbidity and mortality which can be caused with cytotoxic therapy. In addition, the ability to predict response rate based on NGS could substantially decrease the cost related to complications from therapy. Identification of molecular alterations, could change the

47 way a person is treated. For example, if an individual has an alteration in the PI3K/AKT pathway therapy with an mTOR inhibitor might be better than standard chemotherapy or if someone has a BRCA mutation therapy with a PARP inhibitor could also be better.

Evaluating response rates to targeted therapies could give rise to new biomarkers which could be used to improve the understanding of disease, treatment, health surveillance and tracking.

Appendix A: Figures & Tables

Figure 1: Mutations per patient

Total number of significant and variance of unknown significance abnormalities seen per patient

Figure 2: Total number of mutations

Total number of significant and variance of unknown significance abnormalities seen per patient in those presenting with metastatic disease

Figure 3: p53 and Overall Survival

Kaplan meier curve showing overall survival in patients with p53 alterations. Curve on the left shows all patients regardlesss of stage at diagnosis. The curve on the right includes patients who presented with metastatic diagnosis.

Figure 4: Doxorubicin and p53

Kaplan meier curve showing progression free survival in patients treated with doxorubicin who have p53 alterations.

Figure 5: Doxorubicin in non-metastatic patients

Kaplan meier curve showing progression free survival in patients treated with doxorubicin who did not have metastatic disease at diagnosis and have p53 alterations.

The curve on the left shows all patients regardless of stage at diagnosis. The curve on the right includes patients who presented with metastatic disease at diagnosis

Figure 6: Pazopanib and p53

Kaplan meier curve showing progression free survival in patients treated with pazopanib who have p53 alterations. The curve on the left shows all patients regardless of stage at diagnosis. The curve on the right includes patients who presented with metastatic disease at diagnosis.

Figure 7: Gem/tax and p53

Kaplan meier curve showing progression free survival in patients treated with gemcitabine plus docetaxel who have p53 alterations. The curve on the left shows all patients regardless of stage at diagnosis. The curve on the right includes patients who presented with metastatic disease at diagnosis.

Figure 8: BRCA and Overall Survival

On the left is a comparison of patients with BRCA alterations to the entire cohort. On the right is a comparison of patients with BRCA alterations to the rest only in patients with metastatic disease.

Figure 9: Heatmap for all LMS patients

This is a heat map generated using R for all patients with leiomyosarcoma. This dendrogram was used to divide patients into six clusters.

Figure 10: Cluster 2 in OSU and TCGA

This is a comparison between patients who are in cluster 2 versus the remainder in both our dataset and the TCGA.

Figure 11: Cluster 3 in OSU and TCGA

This is a comparison between patients who are in cluster 3 versus the remainder in both our dataset and the TCGA.

Figure 12: Cluster 6 in OSU and TCGA

This is a comparison between patients who are in cluster 6 versus the remainder in both our dataset and the TCGA.

Figure 13: uLMS vs rest of patients

On the left: this is a graph which is comparing patients who have uLMS to other primary site of origin. On the right: This is comparing patients with uLMS to other site of primary tumor origin only in patients with metastatic disease at time of diagnosis.

Figure 14: Heatmap for uLMS patients

This is a heat map generated using R for all patients with uLMS. This dendrogram was used to divide patients into five clusters.

Figure 15: Cluster 2 OSU vs TCGA in uLMS patients

This is a comparison of patients in cluster 2 versus remainder of the cohort in the TCGA

(on left) and our data set (on right).

Figure 16: Cluster 5 OUS vs TCGA in uLMS patients

This is a comparison of patients in cluster 5 versus remainder of the cohort in the TCGA

(on left) and our data set (on right).

Figure 17: RB1 in TCGA vs OSu

Comparison of patients WT RB1 to the remainder (TCGA on left, our data on right)

Table 1: Patient Characteristics

Leiomyosarcoma Total number of patients 61 • Patients with molecular data 58 M:F Ratio 10:48 Median Age 58 Patient with metastatic disease 43 • At diagnosis 28 Total number of genes evaluate 284 • Significant genes 70 Total number of deaths 15

Table 2: Summary of mutations that are seen in patients with BRCA abnormalities

Table 3: Summary of patient characteristics for those with BRCA alterations

Table 4: Summary of patient characteristics for those with BRCA K3326X alterations

Table 5: Summary of patient similarities between our dataset, TCGA and GENIE.

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