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Founded Year

2009

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Unattributed - V | IPO

Total Raised

$28.95M

Market Cap

0.02B

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3.00

About OncoCyte

OncoCyte is a precision diagnostics company that operates in the healthcare sector, specifically focusing on oncology and transplant diagnostics. The company's main offerings include a range of tests designed to provide physicians and their patients with actionable insights at critical decision points in patient care, such as identifying immune checkpoint inhibitor responders, detecting early cancer progression after treatment initiation, and monitoring transplant health through the detection of donor-derived cell-free DNA. OncoCyte primarily serves the healthcare industry. It was founded in 2009 and is based in Irvine, California.

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15 Cushing

Irvine, California, 92618,

United States

949-409-7600

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OncoCyte Patents

OncoCyte has filed 6 patents.

The 3 most popular patent topics include:

  • infectious causes of cancer
  • lung cancer
  • medical imaging
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5/6/2013

Experimental cancer drugs, Monoclonal antibodies, Oncology, Medical imaging, Monoclonal antibodies for tumors

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Application Date

5/6/2013

Grant Date

Title

Related Topics

Experimental cancer drugs, Monoclonal antibodies, Oncology, Medical imaging, Monoclonal antibodies for tumors

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Latest OncoCyte News

A randomized, non-comparative phase 2 study of neoadjuvant immune-checkpoint blockade in retroperitoneal dedifferentiated liposarcoma and extremity/truncal undifferentiated pleomorphic sarcoma

Feb 13, 2024

Abstract Based on the demonstrated clinical activity of immune-checkpoint blockade (ICB) in advanced dedifferentiated liposarcoma (DDLPS) and undifferentiated pleomorphic sarcoma (UPS), we conducted a randomized, non-comparative phase 2 trial ( NCT03307616 ) of neoadjuvant nivolumab or nivolumab/ipilimumab in patients with resectable retroperitoneal DDLPS (n = 17) and extremity/truncal UPS (+ concurrent nivolumab/radiation therapy; n = 10). The primary end point of pathologic response (percent hyalinization) was a median of 8.8% in DDLPS and 89% in UPS. Secondary end points were the changes in immune infiltrate, radiographic response, 12- and 24-month relapse-free survival and overall survival. Lower densities of regulatory T cells before treatment were associated with a major pathologic response (hyalinization > 30%). Tumor infiltration by B cells was increased following neoadjuvant treatment and was associated with overall survival in DDLPS. B cell infiltration was associated with higher densities of regulatory T cells before treatment, which was lost upon ICB treatment. Our data demonstrate that neoadjuvant ICB is associated with complex immune changes within the tumor microenvironment in DDLPS and UPS and that neoadjuvant ICB with concurrent radiotherapy has significant efficacy in UPS. Main Of the ∼13,000 patients diagnosed with soft tissue sarcoma (STS) every year in the United States, more than one-third are expected to die of their disease after current standard management 1 , 2 . Although radiation therapy (RT) and/or chemotherapy reduce recurrence risk, systemic therapy options are limited, highlighting the need for new treatments 2 . As STS is rare and heterogenous with >100 histologic types and subtypes 3 , large, randomized clinical trials are difficult, even in collaboration with other large-volume sarcoma centers. Therefore, new clinical trial designs are necessary to evaluate potential therapies in a timely manner. Within the past decade, major advances have been made in cancer therapy through the use of ICB. Recent evidence suggests that ICB targeting programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte antigen-4 (CTLA-4) has activity in metastatic DDLPS and UPS with variable responses (DDLPS 10–20% and UPS 20–40%) 4 , 5 , 6 , 7 . Our group and others have previously demonstrated improved clinical benefit from ICB in STS with high pretreatment immune infiltration 8 , 9 , particularly by B cells in the context of tertiary lymphoid structures (TLSs) 10 , 11 . Based on these results, we tested the clinical activity of and evaluated immune responses to neoadjuvant ICB in patients with resectable DDLPS and UPS. In this randomized, non-comparative phase 2 trial, patients with treatment-naive primary or locally recurrent resectable retroperitoneal DDLPS (arms A/B; Fig. 1a ) and extremity/truncal UPS (arms C/D; Fig. 1b ) were randomized 1:1 to neoadjuvant nivolumab or nivolumab/ipilimumab. Patients with UPS (arms C/D) were treated with concurrent 50 Gy RT (day (D)15–D50). Following completion of neoadjuvant therapy, all patients underwent surgical resection of the tumor. Fig. 1: Trial schema of immune-checkpoint inhibition in retroperitoneal DDLPS and extremity/truncal UPS. a, Patients with resectable, pathologically confirmed, retroperitoneal DDLPS were randomized at a 1:1 ratio to neoadjuvant nivolumab 3 mg kg−1 intravenously (i.v.) every 14 d for up to three doses (arm A: DDLPS, D1, D15 and D29) or ipilimumab 1 mg kg−1 i.v. one dose plus nivolumab 3 mg kg−1 i.v. every 14 d for up to three doses (arm B: DDLPS, ipilimumab on D1 only, nivolumab D1, D15 and D29), followed by surgical resection 2–4 weeks after the last dose of nivolumab. b, Patients with resectable, pathologically confirmed, extremity/truncal UPS were randomized at a 1:1 ratio to neoadjuvant nivolumab 3 mg kg−1 i.v. every 14 d for up to four doses and concurrent RT starting 2 weeks after the first dose with 50 Gy in 25 fractions (arm C: UPS, nivolumab D1, D15, D29, D43 + RT) or ipilimumab 1 mg kg−1 i.v. one dose plus nivolumab 3 mg kg−1 i.v. every 14 d for up to four doses and concurrent RT starting 2 weeks after first dose with 50 Gy in 25 fractions (arm D: UPS, ipilimumab on D1 only, nivolumab D1, D15, D29, D43 + RT), followed by surgical resection 4–6 weeks after completion of RT. For both treatment arms, the primary end point was pathologic response as assessed by percent hyalinization at surgery. Select secondary end points were percent viable tumor at surgery, change in immune infiltration, ORR by RECIST1.1, RFS, OS and toxicity as assessed by the Common Terminology Criteria for Adverse Events (CTCAE) v.4.0. Select exploratory end points were change in immunologic genomic markers, presence of intratumoral B cells and TLSs and microbiome composition and diversity. To explore those secondary and exploratory end points, longitudinal tumor and blood samples were acquired. Tumor specimens were collected with biopsies before the start of treatment and before the second dose of nivolumab and on surgical specimens. Blood samples were collected before therapy, before each injection of immunotherapy, before surgery and during follow-up. Fecal samples were collected before initiation of therapy, before the second dose of immunotherapy and at surgery. CAP, chest-abdomen-pelvis; CT, computed tomography. The primary end point of the trial was pathologic response (percent hyalinization was a continuous variable) at the time of surgical resection within each treatment arm. Secondary and exploratory end points are listed in Fig. 1 . Correlative end points are presented in patients with available samples for biomarker analysis in each arm. The results of the reported comparisons are exploratory in nature and hypothesis-generating. Results Participants, treatment and toxicity From January 2017 to February 2020, 28 patients were screened for eligibility and consented on trial (Fig. 2 ). One patient was deemed a screen failure. Twenty-seven eligible patients were treated on study: 17 with DDLPS (arm A, n = 8; arm B, n = 9; Fig. 1a ) and 10 with UPS (arm C, n = 6; arm D, n = 4; Fig. 1b ). The initial planned accrual was ten patients in each arm, but the trial was stopped early due to slow accrual and in the context of the COVID-19 pandemic. Patient characteristics and treatment disposition are shown in Table 1 . Eleven patients with DDLPS had recurrent tumors and eight had multifocal disease at baseline; one patient with UPS had recurrent disease. Patients with DDLPS were high risk based on Sarculator-calculated disease-free survival (DFS) (DDLPS 2%, interquartile range (IQR) 1–14; UPS 22%, IQR 20–24) and overall survival (OS) (DDLPS 23%, IQR 15–38; UPS 81%, IQR 79–82) (Table 2 ). Fig. 2: Consolidated Standards of Reporting Trials flow diagram. Flow diagram depicts the disposition of patients throughout the phases of the study, including screening, randomization to neoadjuvant treatment and surgery. Reasons for screen failures, delayed surgery or surgery performed off trial are shown. In DDLPS, although the presence of intratumoral B cells with TLS features by IHC at baseline was not associated with RFS or OS (Extended Data Fig. 8b ), patients with intratumoral B cells with TLS features by IHC at surgery had significantly better OS (median 29.1 months versus non-response, n = 5 of 8 versus 0 of 8, log-rank P = 0.045) and a trend toward longer RFS (median 13.4 months versus 40, n = 7 of 8 versus 4 of 8, log-rank P = 0.14; Fig. 6f ). In UPS, neither of the two patients with baseline infiltration by B cells with TLS features by IHC relapsed and both were alive at the last follow-up (Fig. 6g ). Using RNA-seq data, we found that patients with a higher TLS immune signature (top quartile) had nonsignificant longer RFS (log-rank P = 0.32) and OS (log-rank P = 0.14; Fig. 6h ), which was consistent in each histotype group (Extended Data Fig. 8c,d ). In subgroup analyses based on disease status at baseline (primary versus recurrent) and focality, there was a numerical increase in the number of tumors containing B cells with TLS features in all subgroups. Additionally, patients with a presence of B cells with TLS features at surgery had a nonsignificant improved OS across all the subgroup analyses (Extended Data Fig. 9a–f ). To assess for intratumor heterogeneity in B cells with TLS features, we evaluated surgical resection blocks for a subset of patients and counted the number of lymphoid aggregates for one slide on each available block of the surgical resection (mean of 13 slides per tumor, minimum of 3 slides per tumor and maximum of 49 slides per tumor). Overall, patients who had tumors that were positive for B cells with TLS features had a significantly higher number of lymphoid aggregates (P < 0.01; Extended Data Fig. 9g,h ). Additionally, all slides analyzed for patients with UPS had very low numbers of lymphoid aggregates, with a median of 0 lymphoid aggregates per slide. Discussion Here we report a randomized neoadjuvant trial of ICB in patients with resectable DDLPS and UPS with pathologic response (hyalinization) as the primary end point. The toxicity profile was overall manageable with no new safety concerns. Across tumor types, neoadjuvant ICB trials have shown increased activity and these trial designs have gained a lot of attention 17 , 18 , 19 , 20 , 21 , 22 , suggesting that ICB may be more effective in the earlier localized setting than in the advanced metastatic setting 23 , 24 . In the UPS cohort, notable pathologic responses were observed with concurrent ICB and RT. Historic data from our institution of preoperative RT in 17 extremity/truncal UPS demonstrated a median hyalinization of 17.5% and the pathologic complete response rate (0% viable tumor) of 9% (ref. 25 ). The European Organization for Research and Treatment of Cancer assessed pathologic response and survival after preoperative RT in 100 patients with STS. The median hyalinization was 10% for the whole cohort and for the unclassified sarcoma cohort (n = 34), and 5% for the pleomorphic sarcoma cohort (n = 6) 13 . The median viable tumor at surgery was 30% in the the unclassified sarcoma cohort and 73% in pleomorphic sarcoma 13 . Although our pathologic response data compare very favorably with these reports, a formal prospective comparison between preoperative RT and preoperative combination of RT with concurrent ICB has not been reported. While pathologic response to ICB was not as robust in DDLPS, 1-year RFS was 71%. This is notable as patients with DDLPS in this study are characterized by unfavorable prognostic factors, including high grade, recurrent DDLPS and multifocal disease. Such patients historically have poor overall oncologic outcomes with estimated 6-year DFS of 6.5%, 6-year OS of 32.2% and 1-year DFS after a second surgery for relapse of 50% (ref. 26 ). In contrast, in primary retroperitoneal liposarcoma, a recent phase 3 trial (STRASS) reported a 3-year abdominal RFS of 60.4% and an estimated 1-year abdominal RFS of 70% (ref. 27 ), including low-grade histology with more favorable prognostic factors. Thus, further investigations of neoadjuvant ICB in patients with DDLPS may be warranted, with combination treatments and biomarker-based selection of patients. The higher clinical benefit seen in our trial in patients with UPS compared to DDLPS may not be solely attributed to histology-specific immunosensitivity. RT has known immunomodulating effects 28 and the addition of RT to ICB has demonstrated increased response rates compared to ICB alone in other cancer types 29 . Neoadjuvant RT may be challenging in retroperitoneal diseases; however, the STRASS trial has shown its feasibility with modest benefit in unplanned subgroup analysis when used alone 27 . A French multicenter neoadjuvant trial with sequential ICB and RT is enrolling patients with STS ( NCT03474094 ). The SARC032-SU2C trial ( NCT03092323 ) is a multicenter randomized trial of neoadjuvant RT with or without ICB in resectable DDLPS and UPS 30 . Data from these trials will provide further insight regarding the benefit of RT in combination with ICB in STS. The optimal end point for pathologic response after neoadjuvant therapy in sarcoma is evolving and ill defined. At the time of protocol activation, there were no data regarding pathologic response after neoadjuvant ICB. Others, as well as our group, had identified percent hyalinization as a reasonable surrogate marker for outcomes after neoadjuvant RT 13 , 25 ; this was therefore chosen as the primary end point. In the present trial, we were unable to identify an optimal cutoff for percent hyalinization that was strongly associated with oncologic outcomes, but the trial was not powered to do so. We identified 30% hyalinization as a reasonable surrogate end point for major pathologic response for our correlative analysis. Larger studies of histotype-tailored criteria may be key to define ‘optimal’ cutoffs for future neoadjuvant ICB studies. Previously reported RECIST response rates to ICB in the metastatic setting for DDLPS and UPS range 7–29% 4 , 5 , 31 and are similar in our study; however, we found no correlation between pathologic and radiographic response, highlighting the need for better assessments of response to neoadjuvant therapy in sarcoma. Imaging evaluation in sarcomas has remained a challenge, despite several attempts to address this, including a dedicated neoadjuvant prospective trial with standard of care treatments, comparing several imaging modalities and criteria 32 , 33 . Additionally, imaging evaluation can be more challenging in the neoadjuvant setting compared to the metastatic setting. For instance, the immune-related RECIST evaluation requires confirmation of the response or progression with repeat imaging 4 weeks after the initial evaluation in some cases 34 , which is not feasible in neoadjuvant trials. Immunologic correlative studies recapitulated several known immune biomarkers of response to ICB 35 . Tumor PD-L1 expression in sarcomas is overall associated with worse prognosis and more advanced disease 36 , 37 ; however, PD-L1 is a dynamic biomarker affected by treatments such as preoperative radiation 25 and chemotherapy 38 or a combination of pembrolizumab and talimogene laherparepvec in advanced diseases 39 . In our trial, we found that PD-L1 expression numerically decreased with neoadjuvant ICB in DDLPS and UPS; this may be due to a direct pharmacologic effect of anti-PD-1 or this could also be due to fewer tumor cells present at time of surgery. The predictive impact of baseline expression of PD-L1 with ICB treatment is controversial and no trial to date has displayed a significant association in STS, although it has been noted that responders are more likely to express PD-L1 in the advanced setting 8 , and another trial demonstrating that PD-L1 status may be more informative on treatment rather than before ICB treatment 9 . Likewise, our data may suggest that PD-L1-positive tumors have a longer ‘tail’ on survival curves compared to PD-L1-negative tumors; however, these results are largely nonsignificant. The presence of cytotoxic T cells and the absence of Treg cells at baseline were associated with RFS and OS, particularly in patients with DDLPS. In contrast, data from the SARC028 trial reported that the presence of Treg cells within tumors at baseline in advanced disease was associated with improved RFS with use of pembrolizumab 8 . The contrasting results between these trials is in line with data from other tumor types, where Treg cell infiltration was a negative prognostic marker in most situations but a positive predictive marker in others 35 . This observation warrants further evaluation into the different phenotypes of Treg cells and their interaction with other cells in the tumor microenvironment 40 , 41 , 42 , 43 . Our group previously showed that patients with advanced STS expressing a B cell gene expression signature and characterized by presence of intratumoral TLS had a 50% response to ICB 10 . In the current neoadjuvant trial, we found that the presence of intratumoral B cells at surgery after neoadjuvant ICB treatment was associated with improved OS, whereas their presence at baseline was not associated with prognosis in DDLPS. Notably, baseline tumor infiltration by B cells was associated with higher Treg cell densities but this association was lost upon ICB treatment. Several studies have shown an interaction between Treg cells and TLS, including murine models of fibrosarcoma in which Treg cells impede TLS formation 44 and lung adenocarcinoma, which showed improved tumor control after depletion of Treg cells, predominantly present in TLS 45 . In melanoma improved responses to ICB were reported after depletion of follicular Treg cells, which are Treg cells located in TLS 41 . Notably, the recently published PEMBROSARC trial, also found a prognostic implication of infiltration of TLS by Treg cells 46 . In conclusion, our findings provide evidence that neoadjuvant ICB in combination with RT in UPS is safe with significant pathologic responses and a promising survival benefit. Neoadjuvant ICB in DDLPS and UPS is associated with complex immune changes in the tumor microenvironment including stimulation of TLS and B cells and disruption of associations between B cells and Treg cells. Further studies are needed to optimize these regimens, determine the long-term benefits and fully elucidate the mechanisms of response and resistance. Methods Inclusion and ethics This research complies with all relevant ethical regulations. Written informed consent was provided by all study participants before treatment and this trial adhered to all relevant ethical considerations. The study was approved by The University of Texas MD Anderson Cancer Center’s Institutional Review Board and monitored by the Data and Safety Monitoring Board. Data were collected and analyzed by the investigators and all authors approved and agreed to submit the final manuscript for publication. The authors vouch for the accuracy and completeness of the data and for the fidelity of the trial to the study protocol. This trial was pre-registered on ClinicalTrials.gov on 10 April 2017 under identifier NCT03307616 . Statistics and reproducibility This was an investigator-initiated, randomized, open-label, single institution, noncomparative phase 2 study designed to detect pathologic and immunologic biomarkers of response to ICB in resectable, treatment-naive primary or locally recurrent DDLPS of the retroperitoneum (arms A/B) and UPS of the trunk or extremities (arms C/D). CONSORT guidelines were followed 47 and the study protocol is included in the Supplementary Information . The initial intended accrual was ten patients in each arm; however, the trial was terminated early due to slow accrual and in the context of the COVID-19 pandemic. Patients with treatment-naive primary or locally recurrent retroperitoneal DDLPS were randomized in a 1:1 ratio. In arm A, patients with treatment-naive primary or recurrent retroperitoneal DDLPS received three doses of nivolumab 3 mg kg−1 every 2 weeks on weeks 1, 3 and 5 before surgical resection. In arm B, patients with treatment-naive primary or recurrent retroperitoneal DDLPS received one dose of ipilimumab 3 mg kg−1 combined with nivolumab 1 mg kg−1 on week 1 followed by two doses of nivolumab 3 mg kg−1 every 2 weeks on week 3 and 5 before surgical resection. Participant randomization was implemented by the Clinical Trial Conduct website maintained by the Department of Biostatistics at The University of Texas MD Anderson Cancer Center ( https://biostatistics.mdanderson.org/ClinicalTrialConduct ). Patients with treatment-naive primary or recurrent extremity or truncal UPS were randomized in a 1:1 ratio between arm C and arm D, both of which include combination nivolumab 3 mg kg−1 every 2 weeks on weeks 3, 5 and 7 + 50 Gy RT in 25 fractions. In arm C, patients with UPS received one dose of nivolumab 3 mg kg−1 on week 1 followed by combination nivolumab + RT, as described above. In arm D, patients with UPS received one dose of combination ipilimumab 3 mg kg−1 + nivolumab 1 mg kg−1, followed by combination nivolumab + RT. After a planned data safety monitoring review of the first five patients per group who completed therapy, it was noted that there was increased toxicity in the arms with a combination nivolumab and ipilimumab. Therefore, a protocol addendum was made to change dosing to one dose of ipilimumab 1 mg kg−1 combined with nivolumab 3 mg kg−1 on week 1 in both DDLPS and UPS, which was conducted for the remaining participants in the trial. The primary end point was defined as the percent of hyalinization in the surgical resection specimen in each arm 13 , 25 . With a sample size of 26 (13 per arm) for the DDLPS cohort and a sample size of 14 (7 per arm) for the UPS cohort, the trial would have had 80% power to detect an effect size of 1.145 and 1.632, respectively. Other secondary end points were to assess the change in immune infiltrate in response to neoadjuvant nivolumab monotherapy and neoadjuvant nivolumab and ipilimumab combination therapy, to assess the ORR (defined as rate of patients achieving partial and complete response) of nivolumab monotherapy and nivolumab and ipilimumab combination therapy administered in the neoadjuvant setting as assessed by imaging (RECIST v.1.1 and immune-related response criteria), to assess the 12- and 24-month RFS (defined as the time from surgery to recurrence) and OS (defined as the time from initiation of treatment to death of any cause) and to evaluate the safety of nivolumab monotherapy and combination ipilimumab and nivolumab in the neoadjuvant setting and perioperatively by CTCAE v.4.0 criteria. As pathologic response was not associated with survival, we performed a landmark analysis 1 year after surgery to define clinically meaningful response criteria for analysis of correlates. Early relapsing patients included all patients who had either progressed before surgery or within 52 weeks following surgery: seven patients were considered early relapsing, including five DDLPS and two UPS. Hyalinization at surgery as a continuous variable was not associated with RFS (Cox P = 0.70) nor OS (Cox P = 0.61). To select an optimal cutoff point of hyalinization to define pathologic response, we ran sensitivity analyses. Receiver-operating curves of 1-year RFS prediction found an area under the curve of 0.465 when using hyalinization as a predictor. Two cutoff points were deemed optimal using the criteria that minimized the difference between sensitivity and specificity: 20% and 30% hyalinization. To select one of these two cutoff points, we ran optimal cutoff points by log-rank analysis of RFS; the optimal cutoff point for the whole cohort was 45%, for the DDLPS group was 5% and for the UPS cohort was 30%. Based on these observations, we selected the optimal cutoff point of 30% hyalinization. Additionally, the analyses by the graphical and numerical methods of Lin, Wei and Ying 48 indicate that hyalinization can be analyzed as a linear functional term and proportional hazards assumption was not violated for OS nor RFS. Descriptive statistics (frequency distribution, median (range)) were used to summarize the patient’s characteristics. The primary efficacy end point of pathologic response, assessed at the time of surgical resection by percentage hyalinization, was estimated by the study cohort. The McNemar test was used to determine whether there were differences on a dichotomous dependent variable between two related groups. Nonparametric unpaired tests (Wilcoxon rank-sum and Kruskal–Wallis tests) were used to compare continuous variables between groups and adjusted for multiple comparison by FDR, as required. Comparison between categorical variables were conducted using chi-squared or Fisher’s exact tests, as required. The distributions of RFS and OS were estimated by the Kaplan–Meier method. For events that had not occurred by the time of data analysis, times were censored at the last contact at which the patient was known to be progression or recurrence free for RFS or the last time that the patient was known to be alive for OS. A log-rank test was performed to test the difference in survival between groups. The linear functional form and proportional hazards assumption for hyalinization in the Cox model for RFS and OS were assessed using the graphical and numerical methods of Lin, Wei and Ying 48 . Levene’s test was used to test the homogeneity of variance across patients 49 . SAS v.9.4 and R v.4.1.3 were used to carry out the computations for all analyses. Data were transformed using the dplyr R package v.1.0.8 (ref. 50 ), analyzed using the rstatix R package 51 and plots were generated using the ggpubr R package v.0.4.0 (ref. 52 ) and ggplot2 R package v.3.3.5 (ref. 53 ). Study data were collected and managed using REDCap electronic data-capture tools hosted at The University of Texas MD Anderson Cancer Center 54 , 55 . REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing (1) an intuitive interface for validated data capture; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; and (4) procedures for data integration and interoperability with external sources. Data for correlative studies were collected using Microsoft Excel (v.2016) spreadsheets. Participants Adult participants (>18 years) of all sexes and genders with treatment-naive primary or locally recurrent DDLPS of the retroperitoneum or UPS of the trunk or extremity were eligible for inclusion in this study only if the disease was determined to be surgically resectable and that they were candidates for upfront surgery as agreed by a multidisciplinary consensus (surgical oncology, medical oncology and radiation oncology) after presentation at a sarcoma multidisciplinary conference. Patients must have had recent imaging (CT or magnetic resonance imaging, as appropriate) within 4 weeks of trial enrollment, demonstrating measurable disease as defined by RECIST v.1.1 and at least one tumor amenable to serial biopsy in clinic or be willing to undergo serial biopsies through image-guided procedures during the neoadjuvant phase of the protocol. Patients had to be medically fit to undergo surgery as determined by the treating medical and surgical oncology team, have ECOG performance status 0–2 and intact cardiopulmonary and organ functions. Major exclusion criteria were previous intra-abdominal surgery within 4 weeks of enrollment, previous chemotherapy or targeted therapy for the current sarcoma, previous radiation for sarcoma in the same area, previous or concurrent immunotherapy, previous active malignancy within 2 years previously, active autoimmune disease or current immunosuppressive medication use. Pathologic assessment H&E slides of pretreatment and post-treatment specimens were reviewed by pathologists who specialize in bone and soft tissue tumors (W.L.W. and A.J.L.). Pathologic response was determined on treated surgical specimens by recording the percentage of hyalinization (decreased cellularity with dense collagen deposition), necrosis and residual viable tumor 13 , 56 . PD-L1 staining The IHC study for PD-L1 was performed on 4-μm whole-section formalin-fixed paraffin-embedded (FFPE) unstained slides using the PD-L1 28-8 pharmDx kit (Aglient Dako) on the Dako Autostainer Link 48, according to the manufacturer’s instructions. The percentage of viable tumor cells with any membranous staining and of any intensity was assessed. For tumors to be considered positive for PD-L1, a cutoff of 1% expression was used. The results were plotted using R studio v.3.5.3. Single IHC staining for B cells: CD20/CD21 The 4-μm unstained slides were prepared from representative whole-section FFPE tumor blocks (baseline, on-treatment and surgical samples). IHC staining was performed for CD20 (1:1,400 dilution, Clone L-26, Agilent Dako) using an autostainer (Bond Max, Leica Biosystems). If CD20 staining revealed any B cells, CD21 (1:20 dilution, Clone 2G9, Leica Biosystems) IHC staining was performed to highlight reticular networks where labeling of cells within lymphoid aggregates were suggestive of tertiary lymphoid structures. Multiplex immunofluorescence mIF staining was performed using similar methods as previously described and optimized 57 , 58 . Briefly, 4-mm-thick FFPE sample sections were stained using an mIF panel containing antibodies against CD3 (clone D7A6E, Cell Signaling Technology), CD8 (clone C8/144B, Thermo Fisher Scientific), CD45RO (clone UCHL1, Leica Biosystems), FOXP3 (clone D2W8E, Cell Signaling Technology), PD-1 (clone EPR4877(2), Abcam), KI67 (clone MIB-1, DAKO), PD-L1 (clone E1L3N, Cell Signaling Technology) and CD68 (clone PG-M1 (M), DAKO). All markers were stained in sequence using their respective fluorophore contained in the Opal 7 kit (cat. no. NEL797001KT; Akoya Biosciences) and the individual tyramide signal amplification fluorophores Opal Polaris 480 (cat. no. FP1500001KT) and Opal Polaris 780 kit (cat. no. FP1501001KT, Akoya Biosciences) 57 . The slides were scanned using the Vectra/Polaris v.3.0.3 (Akoya Biosciences) at low magnification, ×10 (1.0 µm per pixel) through the full emission spectrum and using positive tonsil controls from the run staining to calibrate the spectral image scanner protocol 59 . A pathologist selected all the tumor area using regions of interest (ROIs) for scanning in high magnification by the Phenochart Software image viewer v.1.0.12 (931 × 698 µm size at resolution ×20) to capture various elements of tissue heterogeneity. Each ROI was analyzed by a pathologist using InForm v.2.4.8 image analysis software (Akoya Biosciences). Marker colocalization was used to identify specific cells phenotypes in the tumor. Densities of each cell phenotype were quantified and the final data were expressed as number of cells per mm2 (ref. 59 ). All data were consolidated using R studio v.3.5.3 (Phenopter v.0.2.2 packet, Akoya Biosciences). Intratumor heterogeneity FFPE tumor tissue blocks from surgical resections of nine patients with UPS and nine patients with DDLPS were used to build a tissue microarray (TMA) block using the ATA-100 Advanced Tissue Arrayer (Chemicon International). The selection of cases in the DDLPS cohort was based on the density of CD3+ cells calculated by mIF; four patients with the lowest infiltration and five patients with the highest infiltration by mIF at surgery were selected for this TMA. In the UPS cohort, the nine patients who did not progress before surgery were included in this analysis. The TMA block included a total of 90 cores, each measuring 1 mm in diameter. Multi-sampling of the tissue block was performed to account for intratumoral heterogeneity (three random intratumoral areas, one area with high tumor infiltrating lymphocytes (TILs) and another with low TILs (five cores per sample)). IHC studies were performed on 4-μm FFPE sections using a Leica BOND RXm Autostainer. Slides were stained with antibodies targeting human CD8 (clone C8/144B; Thermo Fisher, MS457S), CD163 (clone 10D6; Leica, NCL-L-CD163) and FOXP3 (clone 206D; BioLegend, 320102) using a modified version of the standard Leica Bond DAB ‘F’ IHC protocol. Slides stained for CD8, CD163 and FOXP3 were scored by a board-certified pathologist using digital image analysis software HALO v.3.5 with a modified cytonuclear algorithm to detect the presence of positive cells per area of tissue analyzed. The results were exported as cell density (cells per mm2). Lymphoid aggregates count To evaluate the intratumor heterogeneity of TLS evaluation, we evaluated the presence of lymphoid aggregates on the surgical pathology blocks of a select group of patients. For the DDLPS cohort, we selected the same nine patients as the ones included in the intratumor heterogeneity TMA. For the UPS cohort, we selected the eight patients who had not progressed before surgery and for which TLS evaluation had been conclusive at surgery, as three patients had inconclusive evaluation due to abundant tumor necrosis. An experienced pathologist (R.L.) reviewed H&E whole-section slides for each surgical pathology block to count the lymphoid aggregates in each slide, which were defined as a group of more than 50 lymphoid cells located in the tumor area. RNA extraction and quality control RNA was extracted by a NORGEN Total RNA Purification kit (cat. no. 37500) (Norgen Biotek). Extracted RNA was treated with DNase I. Treated RNA then was cleaned up using AMPure XP beads (Beckman Coulter Life Sciences) and eluted into 1× TE buffer. Purified RNA was quantified using a Quant-iT RiboGreen RNA Assay kit (Thermo Fisher Scientific) and RNA quality was accessed using an Agilent RNA 6000 Nano kit and the 2100 Bioanalyzer Instrument (Agilent Technologies). cDNA synthesis cDNA was prepared from the extracted total RNA using an Ovation RNA-Seq System V2 (NuGEN). Amplification was initiated at the 3′ end as well as randomly throughout the transcriptome in the sample. The prepared cDNA was quantified using Quant-iT PicoGreen dsDNA Assay kit (Thermo Fisher Scientific) and quality was accessed using Genomic DNA ScreenTape and Reagents on the TapeStation 4200 (Agilent Technologies) RNA library preparation Up to 200 ng of each cDNA sample based on the PicoGreen quantification was sheared (mechanically fragmented) using the E220 Focused-ultrasonicator Covaris (Covaris). Sonication was performed under the following conditions: 200 peak incident power, 25% duty cycle, 50 cycles per burst and duration of 10 s for 120 iterations. To ensure the proper fragment size, samples were examined on TapeStation 4200 using the DNA High Sensitivity kit (Agilent Technologies). The sheared cDNA was proceeded to library preparation using SureSelect XT Low Input Reagent kit with indexes 1–96 (Agilent Technologies) as an automated method on the Sciclone G3 NGSx Workstation (PerkinElmer). This protocol consists of three enzymatic reactions for end repair, A-tailing and adaptor ligation, followed by barcode insertion by PCR using Herculase II Fusion DNA Polymerase (8 to 14 cycles, based on input DNA quality and quantity). PCR primers were removed using 1× volume of an Agencourt AMPure PCR Purification kit (Agencourt Bioscience). The quality and quantity of the prepared libraries were evaluated using TapeStation 4200 and a DNA High Sensitivity kit (Agilent Technologies) to verify the correct fragment size and to ensure complete removal of primer dimers. Hybridization and capture Subsequently, prepared libraries were individually hybridized to Agilent SureSelect Human All Exon v.4 probes (Agilent Technologies). The hybridization steps were automated on the Sciclone G3 NGSx Workstation (PerkinElmer). Agilent captures were hybridized as single-sample reactions using 500–1,000 ng prepared library as the input. All hybridization and post-hybridization capture and washes were performed according to Agilent’s protocol. Briefly, capture reagents and probes were added to the prepared libraries, the mixture was incubated at 65 °C on a thermocycler with a heated lid on for up to 24 h. The targeted regions were captured using streptavidin beads and the streptavidin–biotin–probe–target complex was washed and the captured libraries were enriched by PCR amplification according to the manufacturer’s protocol. The quality and quantity of each captured sample was analyzed on TapeStation 4200 using the DNA High Sensitivity kit. RNA sequencing and data analysis The captured libraries were sequenced on Illumina NovaSeq 6000 platform for 2 × 150 paired end reads with an 8-nt read for indexes using Cycle Sequencing v.3 reagents (Illumina). Raw RNA sequence data were processed by an in-house RNA-seq data analysis pipeline, which, among other tools, uses STAR aligner 60 to align raw reads to the hg19 version of the human reference genome, featureCounts 61 to quantify aligned reads with to produce raw counts, Oncofuse 62 to filter and prioritize fusion candidate generated by the STAR aligner and FastQC and QualiMap 63 to evaluate the quality of raw reads and feature counts. We used VirusFinder 64 (v.2.0) to align reads that did not map to the human reference genome to a viral database that contains viruses of 32,102 known classes 65 and in particular all different variations of the Human Papilloma Virus and Polyoma Virus. RNA-seq reads of the samples were mapped to the hg19 reference genome using the STAR aligner 60 . For the calculation of gene expression, the raw count of genes in the samples were converted to transcripts per million that normalize counts for library size and gene length. Similar to Charoentong’s studies 66 , the composition of the immune-infiltrated cells of samples were then generated using ssGSEA enrichment scores of 34 immune gene signatures (gene set signature available in Supplementary Table 2 ). Reporting summary Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article. Data availability The data supporting the findings of this study are available within the manuscript and its supplementary information files. RNA-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession code GSE202361 . All other relevant de-identified data related to the current study are available from the corresponding author (C.L.R.) upon reasonable request (including compelling scientific rationale and preliminary data requiring validation through use of this cohort; these preliminary data should be presented to the authors) and will require the researcher to sign a data access agreement with The University of Texas MD Anderson Cancer Center after approval. Individual patient identifiable clinical data (such as dates) are not publicly available due to concerns with identification of patients. The hg19 human genome can be found at https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.13/ . Source data for all figures and extended data have been provided as Source Data files. Source data are provided with this paper. References Blay, J. Y. et al. Improved survival using specialized multidisciplinary board in sarcoma patients. Ann. Oncol. 28, 2852–2859 (2017). World Health Organization. WHO Classification of Tumours: Soft Tissue and Bone Tumours 5th edn, Vol 3 (International Agency for Research on Cancer, 2020). Tawbi, H. A. et al. Pembrolizumab in advanced soft-tissue sarcoma and bone sarcoma (SARC028): a multicentre, two-cohort, single-arm, open-label, phase 2 trial. Lancet Oncol. 18, 1493–1501 (2017). Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009). Schaefer, I.-M. et al. Histologic appearance after preoperative radiation therapy for soft tissue sarcoma: assessment of the European organization for research and treatment of cancer–soft tissue and bone sarcoma group response score. Int. J. Radiat. Oncol. Biol. Phys. 98, 375–383 (2017). Toulmonde, M. et al. Use of PD-1 targeting, macrophage infiltration, and IDO pathway activation in sarcomas: a phase 2 clinical trial. JAMA Oncol. 4, 93–97 (2018). McGranahan, N. & Swanton, C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168, 613–628 (2017). Hindley, J. P. et al. T-cell trafficking facilitated by high endothelial venules is required for tumor control after regulatory T-cell depletion. Cancer Res. 72, 5473–5482 (2012). Joshi, N. S. et al. Regulatory T cells in tumor-associated tertiary lymphoid structures suppress anti-tumor T cell responses. Immunity 43, 579–590 (2015). Italiano, A. et al. Pembrolizumab in soft-tissue sarcomas with tertiary lymphoid structures: a phase 2 PEMBROSARC trial cohort. Nat. Med. 28, 1199–1206 (2022). Acknowledgements We thank the patients and their families for participating in this study. We thank all the members of our regulatory, clinical, data coordination and translational research teams in the Departments of Surgical Oncology and Sarcoma Medical Oncology at The University of Texas MD Anderson Cancer Center for their support on this trial. The clinical aspects of the study were funded by Bristol-Myers Squibb (drug and funding). Presequencing processing work was carried out by the Moon Shots Platform Cancer Genomics Laboratory, The University of Texas MD Anderson Cancer Center Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy. Sequencing and data generation was supported by a CA016672 (ATGC) grant from The University of Texas MD Anderson Cancer Center, Advanced Technology Genomics Core. This translational analysis was supported by the Rare Tumor Initiative, an MD Anderson Strategic Research Initiative Development Program Microbiome samples were processed by MD Anderson Cancer Center’s Program for Innovative Microbiome and Translational Research. Support for the study was also partially provided by the National Institutes of Health (NIH)/National Cancer Institute (NCI) P30 CA016672 Cancer Center Support Grant. J.A.W. is supported by the NIH (1 R01 CA219896-01A1), US–Israel Binational Science Foundation (201332), the Melanoma Research Alliance (4022024), American Association for Cancer Research Stand Up To Cancer (SU2C-AACR-IRG-19-17), Department of Defense (W81XWH-16-1-0121), MD Anderson Cancer Center Multidisciplinary Research Program Grant, Andrew Sabin Family Fellows Program and MD Anderson Cancer Center’s Melanoma Moon Shots Program. C.L.R. received support from NIH/NCI The Paul Calabresi K12 Career Development Award CA088084-16A1, The Society of Surgical Oncology Clinical Investigator Award and The American College of Surgeons Faculty Research Fellowship. E.F.N.H. received support from the LMS SPORE Career Enhancement Program, the QuadW foundation, Sarcoma Foundation of America, Fondation pour la Recherche Medicale and Fondation Nuovo-Soldati. E.Z.K. received grant support from the QuadW foundation, Sarcoma Foundation of America, Fondation pour la Recherche Medicale and the NCI Early Surgeon Scientist Program. Author information Competing interests This study was supported by Bristol-Myers Squibb. A.J.L. has served on advisory boards and/or consulted for AbbVie, Adaptimmune, ArcherDX, AstraZeneca, Bayer, BMS, Deciphera Pharmaceuticals, Foghorn Therapeutics, Gothams, GSK, Guardant, Invitae, Illumina, Iterion Therapeutics, Merck, Novartis, Nucleai, Paige.AI, Pfizer, Roche/Genentech and Thermo Fisher. K.K.H. is on the medical advisory board for Armada Health and AstraZeneca and reports research funding to The MD Anderson Cancer Center from Cairn Surgical, Eli Lilly & Co. and Lumicell. D.A. receives research funding from Adaptimmune, GSK and Immatics. H.T. received grant or research support from BMS, Novartis, Merck, Genentech, GlaxoSmithKline, EMD Sereno, Eisai, Dragonfly Therapeutics, RAPT Therapeutics; and is a consultant for BMS, Genentech, Novartis, Merck, Boxer Capital, Karyopharm, Iovance, Eisai, Jazz Pharmaceuticals and Medicenna. R.G.W. is supported by the NIH T32 CA 009599 and The MD Anderson Cancer Center support grant P30 CA016672 . J.Y.B. has received research support and honoraria from Roche, GlaxoSmithKline, BMS and MSD. W.H.F. is a consultant for Novartis, Adaptimmune, Anaveon, Catalym, OSE Immunotherapeutic, Oxford Biotherapeutics, Genenta and Parthenon. K.S. is a consultant for Guidepoint, GLG, BlueprintBiomedicines and Coleman and is on the editorial committee for a CSHP publication. I.W. has provided consulting or advisory roles for AstraZeneca/MedImmune, Bayer, Bristol-Myers Squibb, Genentech/Roche, GlaxoSmithKline, Guardant Health, HTG Molecular Diagnostics, Merck, MSD Oncology, OncoCyte, Jansen, Novartis, Flame and Pfizer; has received grants and personal fees from Genentech/Roche, Bristol-Myers Squibb, AstraZeneca/MedImmune, HTG Molecular, Merck and Guardant Health; has received personal fees from GlaxoSmithKline and Oncocyte, Daiichi Sankyo, Roche, AstraZeneca, Pfizer and Bayer; and has received research funding to his institution from 4D Molecular Therapeutics, Adaptimmune, Adaptive Biotechnologies, Akoya Biosciences, Amgen, Bayer, EMD Serono, Genentech, Guardant Health, HTG Molecular Diagnostics, Iovance Biotherapeutics, Johnson & Johnson, Karus Therapeutics, MedImmune, Merck, Novartis, OncoPlex Diagnostics, Pfizer, Takeda and Novartis. J.A.W. reports compensation for speaker’s bureau and honoraria from Imedex, Dava Oncology, Omniprex, Illumina, Gilead, PeerView, Physician Education Resource, MedImmune and Bristol-Myers Squibb; is on the advisory board as a consultant for Roche/Genentech, Novartis, AstraZeneca, GlaxoSmithKline, Bristol-Myers Squibb, Merck, Micronoma and Biothera Pharmaceuticals; and has stock options for Micronoma. N.S. is on the advisory board as a consultant for Deciphera, AADI Biosciences, Epizyme and Boehringer Ingelheim; receives research funding from Decipehra, Daiichi Sankyo, Karyopharm, AstraZeneca, Cogent Biosciences, Ascentage and GSK; and has an immediate family member with stock options from Pfizer and JNJ. Peer review Peer review information Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Extended data

OncoCyte Frequently Asked Questions (FAQ)

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    OncoCyte's latest funding round is Unattributed - V.

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    OncoCyte raised a total of $28.95M.

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