Search

Background In 2020, pembrolizumab was approved as a therapy for triple-negative breast cancer (TNBC) with the companion diagnostic DAKO 22C3 programmed death ligand-1 (PD-L1) immunohistochemistry assay. The study aimed to determine the landscape of PD-L1 expression as detected by the DAKO 22C3 PD-L1 assay in breast cancer subtypes and compare the clinicopathologic and genomic characteristics of PD-L1 positive and negative TNBC. Methods PD-L1 expression using the DAKO 22C3 antibody was scored using a combined positive score (CPS) and positive status was defined as CPS ≥10. Comprehensive genomic profiling was performed using the FoundationOne CDx assay. Results Of the 396 BC patients stained with DAKO 22C3, the majority were HR+/HER2− and TNBC (42% and 36%, respectively). Median PD-L1 expression and frequency of CPS ≥10 was highest in TNBC cases (median: 7.5, 50% CPS ≥10) and lowest in the HR+/HER2− group (median: 1.0, 15.5% CPS ≥10) (P Conclusions The subtypes of breast cancer have distinct patterns of PD-L1 expression, supporting that further research of immunotherapies may include specific evaluation of optimum cutoffs for non-TNBC patients. In TNBC, PD-L1 positivity is not associated with other clinicopathologic or genomic features and should be integrated into future studies of immunotherapy efficacy.

Background The status of CDH1, ERBB2, and ESR1 is important for the diagnostic and treatment workup for patients with breast cancer. Most alterations in these genes occur in the form of short variants (eg missense and indel alterations) or copy number alterations (eg amplifications of ERBB2 or deletions in CDH1). The prevalence and characteristics of rearrangement events have been understudied. Materials and Methods: Comprehensive genomic profiling using a hybrid-capture based approach was performed on 44,842 breast carcinomas during the course of routine clinical care (FoundationOne® or FoundationOne®CDx) examining all classes of alterations in up to 395 genes, including CDH1, ESR1, and ERBB2. All rearrangements were included in the analysis (known/likely pathogenic and variants of uncertain significance). Estrogen receptor status was extracted from pathology reports for a subset of samples. Results: Rearrangements in CDH1, ESR1, and ERBB2 were observed in 0.26% (115/44842), 0.34% (153/44842), and 1.33% (598/44842) of breast cancer samples, respectively. As expected, CDH1 rearrangements were most common in invasive lobular carcinoma (ILC) (0.64%; 16/2516) though events were observed in samples originally submitted as invastive ductal carcinoma (IDC) (0.16%; 26/15,836), suggesting possible misdiagnosis. CDH1 rearrangements were predominantly loss of function consisting of large deletions, inversions, and truncation events. ESR1 rearrangements were observed at the highest frequency in ER+/HER2- tumors (0.58%) and were never seen in ER-/HER2- and ER-/HER2+ tumors. ESR1 rearrangements were observed with a variety of partners, with recurrent events with CCDC170, SYNE1, RMND1, PLEKHG1, ARMT1, MTHFD1L, and ZBTB2. Consistent with a possible role in therapy resistance, ESR1 rearrangements were enriched in metastatic samples relative to those biopsied from the breast (OR = 2.25; p = 8E-05). ERBB2 rearrangement events were commonly observed in HER2 amplified tumors (13.4%) and rarely in other subtypes (0.12% in ER+/HER2- and 0.28% in ER-/HER2-). Most of the events were intra-chromosomal and typically represented non-fusion duplication fragments that may have generated the ERBB2 amplification. Fusions were much rarer. Out of the 598 rearrangement events, only 18 were predicted to create in-frame and in-strand fusion products retaining the HER2 kinase domain following manual review of the events. Most fusion events were unique, though a fusion with IKZF3 was seen recurrently (n=2). Conclusions Rearrangement events in diagnostically important breast cancer genes (CDH1, ESR1, and ERBB2) were commonly observed in breast cancer with subtype-specific enrichment. Since these alterations have implications in disease diagnosis and therapy response (eg endocrine therapy resistance), comprehensive genomic profiling can provide value in breast cancer care. Citation Format: Ethan Sokol, Dexter Jin, Jeffrey S. Ross, ADRIAN V. LEE, Steffi Oesterreich. Rearrangements in CDH1, ESR1, and ERBB2 are commonly observed in breast cancer and may influence diagnosis and treatment [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-14-11.

Background: Malignant phyllodes tumors (MPT) are rare fibroepithelial breast tumors that disproportionately affect young women. Despite high local and distant recurrence rates, treatment is almost exclusively surgical. With no known effective chemotherapy or targeted systemic therapy options, metastatic progression portends dismal prognosis. Current national guidelines do not recommend biomarker testing in the non-metastatic setting. Management of metastatic disease follows principles of soft tissue sarcoma recommending Next-Generation Sequencing on an individual basis for patients who may qualify for clinical trials or are refractory to standard therapies. In the largest genomic profiling effort of MPT to date, we sought to describe the genomic landscape of MPTs through comprehensive genomic profiling (CGP) and immunotherapeutic biomarker analysis. Methods: Cases of sequenced MPT were identified from a CLIA-certified, CAP-accredited laboratory database (Foundation Medicine, Cambridge, MA). Cases were categorized as localized/locally recurrent or metastatic. All cases underwent CGP by DNA extraction from FFPE samples using a hybrid capture, adaptor ligation-based next generation sequencing assay, and all classes of genomic alterations, microsatellite instability (MSI), and tumor mutational burden (TMB) were evaluated. PD-L1 expression by IHC was measured by the DAKO 22C3 assay (scored CPS) or the Ventana SP142 assay (scored as IC). Patient characteristics and results of CGP of MPT were summarized by either N (%) or median (range). Factors from MPT were compared to all cases of breast carcinoma in the lab database. Fisher’s Exact Tests were used to test for differences between groups and analysis of variance was used to test for differences for continuous variables. Results: Of 135 MPT cases identified; 94 (69.6%) were localized/locally recurrent (breast, chest wall, soft tissue, skin, or lymph node) and 41 (30.4%) were metastatic (73% lung, followed by bone, liver, other intraabdominal). All patients were female with a median age of 54 years (range 14-86). The median TMB across samples was 2.5 mut/Mb and only 3 were TMB-high tumors (>10mut/Mb). Over 1/3 (36.8%) of samples were PD-L1+ via Ventana SP142 assay (≥1) and 21.4% were PD-L1+ via Dako 22C3 assay (CPS ≥10). All evaluable cases (N=132) were microsatellite stable. The ten most commonly altered genes included TERT-promoter (69.7%), CDKN2A (45.9%), TP53 (37.8%), NF1 (35.6%), CDKN2B (33.3%), MED12 (28.9%), MTAP (27.7%), KMT2D (22.2%), PIK3CA (20.0%), PTEN (18.5%), and RB1 (18.5%); notably distinct in frequency from the breast carcinoma cohort (Table 1). Alterations in genes affecting cell cycle regulation (i.e. CDKN2A/B and TP53) were mutually exclusive from one another (p< 0.001). Several tumors harbored genomic alterations with FDA-approved indications in other tumor types were found including: NF1, PIK3CA, EGFR Exon 19/20 insertions, and BRAF V600E mutations. There were no statistically significant differences between alterations in localized/locally recurrent versus metastatic specimens. Conclusions: In the largest genomic evaluation of localized/locally recurrent or metastatic MPTs to date, multiple clinically actionable mutations for further exploration were found. TERT-promoter was altered in the majority of cases. PDL-1 was expressed in over 1/3 of cases suggesting that clinical investigation of immunotherapeutic strategies is warranted. Routine sequencing of metastatic MPT may provide additional information to guide treatment decisions and clinical trial enrollment. Table 1. Frequency of Genomic Alterations identified in 135 cases of Malignant Phyllodes Tumors. Citation Format: Laura H. Rosenberger, Richard F. Riedel, Natalie A. Danziger, Ethan Sokol, Jeffrey S. Ross, Sarah L. Sammons. Genomic Evaluation of Malignant Phyllodes Tumors Reveals Multiple Targetable Opportunities [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-23-03.

Purpose: The PURE-01 study (NCT02736266) pioneered the neoadjuvant immune-checkpoint inhibitor (ICI) therapy before radical cystectomy (RC) in patients with muscle-invasive urothelial bladder carcinoma (MIBC). We herein present the survival outcomes after a median follow-up of three years. Patients and Methods: The intention-to-treat (ITT) population included 155 patients. Event-free survival (EFS) was defined as the time from pembrolizumab initiation until radiographic disease progression precluding RC, initiation of neoadjuvant chemotherapy, recurrence after RC, or death. Further outcomes were recurrence-free survival (RFS) post-RC and overall survival (OS). Multivariable Cox regression analyses for EFS were performed. Kaplan–Meier analyses compared EFS outcomes according with baseline programmed cell-death-ligand-1 (PD-L1) combined positive score (CPS) and according to the molecular subtypes. Results: After a median (interquartile range, IQR) follow-up of 39 (30–47) months, 36-month EFS and OS were 74.4% [95% confidence interval (CI), 67.8–81.7] and 83.8% (95% CI, 77.8–90.2) in the ITT population, respectively. Overall, 143 (92.3%) patients underwent RC. Within the cohort of patients who did not receive additional chemotherapy (N = 125), 36-month RFS was 96.3% (95% CI, 91.6–100) for patients achieving a ypT0N0, 96.1% (95% CI, 89–100) for ypT1/a/isN0, 74.9% (95% CI, 60.2–93) for ypT2–4N0, and 58.3% (95% CI, 36.2–94.1) for ypTanyN1–3 response. EFS was significantly stratified among PD-L1 tertiles (lower tertile: 59.7% vs. medium tertile: 76.7% vs. higher tertile: 89.8%, P = 0.0013). The claudin-low and basal/squamous subtypes displayed the lowest rates of events. Conclusions: At a median follow-up of three years, PURE-01 results further confirm the sustained efficacy of neoadjuvant pembrolizumab before RC. PD-L1 expression was the strongest predictor of sustained response post-RC.

Patients with advanced gastroesophageal cancer (mEG) and tumor mutational burden ≥10 mut/Mb (TMB ≥ 10) have more favorable outcomes on immune checkpoint inhibitor (ICPI) monotherapy compared with chemotherapy in subgroup analyses of randomized controlled trials. We sought to evaluate the robustness of these associations in real-world settings where patients and practices are more diverse. A total of 362 2 L and 692 1 L patients, respectively received ICPI (n = 99, 33) or chemotherapy (n = 263, 659) across approximately 280 U.S. academic or community-based cancer clinics March 2014–July 2021. Deidentified data were captured into a real-world clinico-genomic database. All patients underwent Foundation Medicine testing. Time to next treatment (TTNT) and overall survival (OS) comparing ICPI versus chemotherapy were adjusted for treatment assignment imbalances using propensity scores. 2L: TMB ≥ 10 had more favorable TTNT [median 24 vs. 4.1 months; HR: 0.19; 95% confidence interval (CI): 0.09–0.44; P = 0.0001] and OS (median 43.1 vs. 6.2 months; HR: 0.24; 95% CI: 0.011–0.54; P = 0.0005), TMB < 10 did not (P > 0.05). 1L: TMB ≥ 10 had more favorable TTNT (not reached vs. median 4.1 months; HR: 0.13; 95% CI: 0.03–0.48; P = 0.0024) and OS (not reached vs. median 17.1 months; HR: 0.30; 95% CI: 0.08–1.14; P = 0.078), TMB < 10 had less favorable TTNT (median 2.8 vs. 6.5 months; HR: 2.36; 95% CI: 1.25–4.45; P = 0.008) and OS (median 4.5 vs. 13.1 months; HR: 1.82, 95% CI: 0.87–3.81; P = 0.11). TMB ≥ 10 robustly identifies patients with mEG with more favorable outcomes on 2 L ICPI monotherapy versus chemotherapy. 1 L data are more limited, but effects are consistent with 2L. Significance: Using real-world data, we sought to evaluate robustness of these clinical associations using the same assay platform and biomarker cut-off point used in both clinical trials and pan-tumor CDx approvals for later treatment lines. TMB ≥ 10 robustly identified patients with mEG with more favorable outcomes on ICPI monotherapy versus chemotherapy and suggests this subset of patients could be targeted for further trial development.

Oncocytic histologic features can be seen in a variety of salivary gland carcinomas. We performed a comprehensive molecular profiling of 15 salivary gland malignancies with oncocytic features (diagnosed as oncocytic carcinoma, carcinoma NOS with oncocytic features, or salivary duct carcinoma with oncocytic features). We reveal multiple novel molecular alterations that have not been previously described in other salivary gland malignancies, including, but not limited to, KEL amplification (13.3%, 2/15), PARP1 amplification (13.3%, 2/15), and EPHB4 amplification (13.3%, 2/15). Alterations in KMT2C (13.3%, 2/15), ERBB3 (13.3%, 2/15), CTNNA1 (13.3%, 2/15), and SMAD4 (20%, 3/15) were also found in this series and have been reported in other salivary gland malignancies. Alterations that have been reported in salivary duct carcinoma were also identified, including TP53 (40%, 6/15) , ERBB2 mutations (13.3%, 2/15) , ERBB2 amplification (13.3%, 2/15), PIK3CA (26.7%, 4/15) , PTEN (20%, 3/15), BRCA2 (20%, 3/15), BRAF (20%, 3/15), CDKN2A/B (20%, 3/15), CDH1 (13.3%, 2/15), and HRAS (13.3%, 2/15). Oncocytic salivary gland malignancies are a molecularly heterogenous group of tumors with partial overlap with salivary duct carcinoma subtypes.

Background Advanced pelvic squamous cell carcinoma (pSCC) is a broad category of cancers affecting different pelvic organs and usually featuring unfavorable clinical outcomes. Thus, we aimed to assess genomic differences among pSCC cases and learn whether pSCC could potentially benefit from targeted therapies and/or immunotherapy. Materials and Methods A total of 1917 advanced pSCCs, including penile (penSCC), male urethral (murthSCC), male anal (manSCC), female urethral (furthSCC), vulvar (vulSCC), cervical (crvSCC), female anal (fanSCC), and vaginal (vagSCC), underwent comprehensive genomic profiling (CGP). We used hybrid capture-based CGP to evaluate recurrent genomic alterations (GAs). Tumor mutational burden (TMB) was determined on up to 1.1 Mb of sequenced DNA and microsatellite instability (MSI) was determined on up to 95 loci. Programmed cell-death-ligand-1 (PD-L1) expression was determined by immunohistochemistry (IHC; Dako 22C3). Results PIK3CA was the most frequently identified potentially “actionable” GA (22%-43%), followed by mTOR pathway [PTEN (0%-18%), FBXW7 (7%-29%)], and cell-cycle GAs. DNA-damage response (DDR) GAs and receptor-tyrosine kinase (RTK) targeted options were uncommon. NOTCH1 GAs were present in >15% of penSCC and vulvSCC. TMB ≥10 mut/Mb was >15% in manSCC, fanSCC, crvSCC, and vagSCC. PD-L1 high expression was >18% in all pSCC except urthSCC, manSCC, and vagSCC. HPV-16/18 detection was highest in manSCC, fanSCC, and crvSCC. Conclusion Despite similar histology, pSCCs can differ in GAs and HPV status. Overall, PIK3CA is the most frequent potentially “targetable” GA followed by mTOR and cell cycle pathway. RTK and DDR GAs are rare in pSCC. Immunotherapy could be considered for pSCC management based on TMB and PD-L1 expression.

Background Despite the low rate of urothelial carcinoma of the bladder (UCB) in patients of South Asian (SAS) and East Asian (EAS) descent, they make up a significant portion of the cases worldwide. Nevertheless, these patients are largely under-represented in clinical trials. We queried whether UCB arising in patients with SAS and EAS ancestry would have unique genomic features compared to the global cohort. Methods Formalin-fixed, paraffin-embedded tissue was obtained for 8728 patients with advanced UCB. DNA was extracted and comprehensive genomic profiling was performed. Ancestry was classified using a proprietary calculation algorithm. Genomic alterations (GAs) were determined using a 324-gene hybrid-capture-based method which also calculates tumor mutational burden (TMB) and determines microsatellite status (MSI). Results Of the cohort, 7447 (85.3%) were EUR, 541 (6.2%) were AFR, 461 (5.3%) were of AMR, 74 (0.85%) were SAS, and 205 (2.3%) were EAS. When compared with EUR, TERT GAs were less frequent in SAS (58.1% vs. 73.6%; P = .06). When compared with non-SAS, SAS had less frequent GAs in FGFR3 (9.5% vs. 18.5%, P = .25). TERT promoter mutations were significantly less frequent in EAS compared to non-EAS (54.1% vs. 72.9%; P < .001). When compared with the non-EAS, PIK3CA alterations were significantly less common in EAS (12.7% vs. 22.1%, P = .005). The mean TMB was significantly lower in EAS vs. non-EAS (8.53 vs. 10.02; P = .05). Conclusions The results from this comprehensive genomic analysis of UCB provide important insight into the possible differences in the genomic landscape in a population level. These hypothesis-generating findings require external validation and should support the inclusion of more diverse patient populations in clinical trials.

Summary of TMB ranges per cohort and MSI (TMB10+ only). The median and interquartile range of TMB is shown per group within the cohorts, grouped by MSI status.

Discriminatory Power Comparison of MSI and TMB. The concordance indexes from univariable Cox PH models containing NGS-based MSI assessment, TMB, or the two in a combined multivariable Cox PH model is shown with (A) TTNT and (B) OS. Error bars indicate standard error.

Propensity Weighting and Balance Adjustment: TMB. The pre- and post-adjustment balance for (A) 2nd line cohort with TMB < 10, (B) 2nd line cohort with TMB 10 or greater, (C) 1st line cohort with TMB < 10, (D) 1st line cohort with TMB 10 or greater.

Sequential TTNT treatment-TMB interaction model from Figure 3. The treatment interaction model from Figure 3C is shown numerically.

TMB ≥ 10 treatment predictive associations is not approximated by PD-L1 CPS ≥ 10. Cox PH models containing treatment interaction terms for both TMB ≥ 10 and PD-L1 CPS ≥ 10 are shown numerically (A) 2nd line cohort TTNT, (B) 2nd line cohort OS, and (C) sequential cohort TTNT.

Unadjusted 2nd line treatment-TMB interaction models from Supplemental Figure 2. The (A) TTNT and (B) OS interaction models are shown for for propensity adjusted analyses in Supplemental Figure 2.

Patient demographics in the second line patient cohort.

Unadjusted 1st line treatment-TMB interaction models from Supplemental Figure 3. The (A) TTNT and (B) OS interaction models are shown for for propensity adjusted analyses in Supplemental Figure 3.

Restricted Mean Survival Time Difference by TMB Status in the 2L and 1L Comparative Effectiveness Cohorts. Conditional average treatment effects are defined in each TMB subgroup as the difference in expected survival time up to 3 years when treated with ICPI vs chemo. For instance, within the 1st 3 years after 2nd line treatment initiation, patients with TMB10+ would be expected to have a mean of 15.4 more months until treatment switching on ICPI compared to chemo. Bootstrap 95% confidence intervals (q025, q975) were estimated with 5000 resampling replicates.

1st line treatment-TMB interaction models from Figure 5. The (A) TTNT and (B) OS interaction models are shown for for propensity adjusted analyses in Figure 5.

Summary of TMB ranges per cohort and PD-L1. The median and interquartile range of TMB is shown per group within the cohorts, grouped by PD-L1 status.

Unadjusted for known treatment assignment imbalances, patients receiving 2nd line ICPI vs. chemotherapy have more favorable outcomes when TMB ≥ 10 but not TMB < 10. Kaplan-Meier curves are unadjusted for imbalances. TTNT is shown by drug class for (A) TMB < 10, and (B) TMB ≥ 10. OS is shown by drug class for (C) TMB < 10, and (D) TMB ≥ 10. X-axis is truncated at 36 months. Overall survival estimates are left truncated (see Methods) with at-risk tables adjusted accordingly. Interaction models can be found in Supplemental Figure 8.

Unadjusted for known treatment assignment imbalances, patients receiving 1st line ICPI vs. chemotherapy have more favorable outcomes when TMB ≥ 10 but not TMB < 10. Kaplan-Meier curves are unadjusted for imbalances (propensity weights applied). TTNT is shown by drug class for (A) TMB < 10, and (B) TMB ≥ 10. OS is shown by drug class for (C) TMB < 10, and (D) TMB ≥ 10. X-axis is truncated at 36 months. Overall survival estimates are left truncated (see Methods) with at-risk tables adjusted accordingly. Interaction models can be found in Supplemental Figure 9.

2nd line treatment-TMB interaction models from Figure 2. The (A) TTNT and (B) OS interaction models are shown for for propensity adjusted analyses in Figure 2.

Patients with advanced gastroesophageal cancer (mEG) and tumor mutational burden ≥10 mut/Mb (TMB ≥ 10) have more favorable outcomes on immune checkpoint inhibitor (ICPI) monotherapy compared with chemotherapy in subgroup analyses of randomized controlled trials. We sought to evaluate the robustness of these associations in real-world settings where patients and practices are more diverse. A total of 362 2 L and 692 1 L patients, respectively received ICPI (n = 99, 33) or chemotherapy (n = 263, 659) across approximately 280 U.S. academic or community-based cancer clinics March 2014–July 2021. Deidentified data were captured into a real-world clinico-genomic database. All patients underwent Foundation Medicine testing. Time to next treatment (TTNT) and overall survival (OS) comparing ICPI versus chemotherapy were adjusted for treatment assignment imbalances using propensity scores. 2L: TMB ≥ 10 had more favorable TTNT [median 24 vs. 4.1 months; HR: 0.19; 95% confidence interval (CI): 0.09–0.44; P = 0.0001] and OS (median 43.1 vs. 6.2 months; HR: 0.24; 95% CI: 0.011–0.54; P = 0.0005), TMB < 10 did not (P > 0.05). 1L: TMB ≥ 10 had more favorable TTNT (not reached vs. median 4.1 months; HR: 0.13; 95% CI: 0.03–0.48; P = 0.0024) and OS (not reached vs. median 17.1 months; HR: 0.30; 95% CI: 0.08–1.14; P = 0.078), TMB < 10 had less favorable TTNT (median 2.8 vs. 6.5 months; HR: 2.36; 95% CI: 1.25–4.45; P = 0.008) and OS (median 4.5 vs. 13.1 months; HR: 1.82, 95% CI: 0.87–3.81; P = 0.11). TMB ≥ 10 robustly identifies patients with mEG with more favorable outcomes on 2 L ICPI monotherapy versus chemotherapy. 1 L data are more limited, but effects are consistent with 2L.Significance:Using real-world data, we sought to evaluate robustness of these clinical associations using the same assay platform and biomarker cut-off point used in both clinical trials and pan-tumor CDx approvals for later treatment lines. TMB ≥ 10 robustly identified patients with mEG with more favorable outcomes on ICPI monotherapy versus chemotherapy and suggests this subset of patients could be targeted for further trial development.

Summary of TMB ranges per cohort and MSI. The median and interquartile range of TMB is shown per group within the cohorts, grouped by MSI status.

Patient demographics in 1st Line Cohort

TMB and PD-L1 status and strata in the 2L and sequential cohorts.

PDF file - 106KB, Schematic representation of the study design. 37 IMT tumor samples from 33 patients were collected under IRB approved protocols. ALK immunohistochemistry (IHC) was performed on each of the 37 cases as standard of clinical care. Targeted next-generation sequencing (NGS) using the FoundationOne was completed in each case. 22/26 ALK IHC+ and 11/11 ALK IHC- samples were evaluable. ALK kinase fusions were detected in 20/22 (91%) of the ALK IHC+ cases. In the ALK IHC- cases, 2/11 (18%) harbored ALK fusions, 2/11 (18%) harbored PDGFRβ fusions, and 4/11 (36%) harbored ROS1 fusions. Overall, therapeutically actionable kinase fusions were detected in 28/33 (85%) of cases.

XLSX - 13K, Table 1a. 182 genes across entire coding sequence Table 1b. 14 genes sequenced across selected introns.

TNFAIP8 knockdown inhibits cell migration

Supplementary Figure 1. Somatic mutations detected in histiocytic neoplasms. Supplementary Figure 2. Frequencies of known activating kinase mutations in histiocytic neoplasms. Supplementary Figure 3. ARAF mutations and variants of unknown significance detected in BRAFV600E-wildtype, non-Langerhans Cell Histiocytic neoplasms. Supplementary Figure 4. Clinical and histological images of the non-Langerhans cell histiocytosis (non-LCH) lesions from index patients with kinase fusions identified by RNA-seq. Supplementary Figure 5. Gene expression analysis of histiocytic neoplasms by RNA-seq. Supplementary Table 1. Characteristics of patient samples with histiocytic neoplasms used in this study and genomic analysis utilized for each sample. Supplementary Table 2. Whole exome sequencing metrics. Supplementary Table 3. Somatic variants identified by whole exome and whole transcriptome sequencing and validated by droplet-digital PCR and/or custom-capture targeted next-generation sequencing with variant allele frequencies (VAFs). Supplementary Table 4. List of the top 1% of differentially expressed genes across histiocytic samples from mRNA sequencing. Supplementary Table 5. PCR primers with M13F2 and M13R2 tails (blue) for Sanger sequencing used in the targeted sequencing recurrence testing for MAP2K1 exons 2 and 3 and all coding regions of ARAF.

XLSX - 22K, Substitution, insertion and deletion mutations/large structural alterations.

Supplementary Methods, Supplementary References, Supplementary Table Legends, and Supplementary Figure Legends.

Supplementary Table S3: Genomic alterations across selected targets. See Figure 3 for additional details. Zoomable PDF.

qRT-PCR assay

PDF - 138K, Adjuvant systemic treatments.

IGFBP3 in culture medium

Therapeutic antibodies blocking programmed death-1 and its ligand (PD-1/PD-L1) induce durable responses in a substantial fraction of melanoma patients. We sought to determine whether the number and/or type of mutations identified using a next-generation sequencing (NGS) panel available in the clinic was correlated with response to anti–PD-1 in melanoma. Using archival melanoma samples from anti–PD-1/PD-L1-treated patients, we performed hybrid capture–based NGS on 236–315 genes and T-cell receptor (TCR) sequencing on initial and validation cohorts from two centers. Patients who responded to anti–PD-1/PD-L1 had higher mutational loads in an initial cohort (median, 45.6 vs. 3.9 mutations/MB; P = 0.003) and a validation cohort (37.1 vs. 12.8 mutations/MB; P = 0.002) compared with nonresponders. Response rate, progression-free survival, and overall survival were superior in the high, compared with intermediate and low, mutation load groups. Melanomas with NF1 mutations harbored high mutational loads (median, 62.7 mutations/MB) and high response rates (74%), whereas BRAF/NRAS/NF1 wild-type melanomas had a lower mutational load. In these archival samples, TCR clonality did not predict response. Mutation numbers in the 315 genes in the NGS platform strongly correlated with those detected by whole-exome sequencing in The Cancer Genome Atlas samples, but was not associated with survival. In conclusion, mutational load, as determined by an NGS platform available in the clinic, effectively stratified patients by likelihood of response. This approach may provide a clinically feasible predictor of response to anti–PD-1/PD-L1. Cancer Immunol Res; 4(11); 959–67. ©2016 AACR.

PDF file - 77KB

Supplementary Figure 1 shows RICTOR amplification/overexpression in the index patient by FISH and IHC. Supplementary Figure 2 shows analysis of TCGA database for RICTOR alteration in all types of cancers, including NSCLC. Supplementary figure 3A shows the gene alterations in each individual with RICTOR-amplified tumors. 3B shows the absolute and relative frequency of each gene alteration found in RICTOR-amplified lung cancer samples from FM NGS database.

Supplementary Tables S1 - S3. Supplementary Table S1. Summary of EGFR alterations in NSCLC identified by FoundationOne. Supplementary Table S2. Summary of genomic coordinates for the kinase fusions identified in this study. Supplementary Table S3. Results of MTT curve fitting from Prism.

Supplementary Fig. S3: Co-mutation across all PACC tumors.

Additional detailed methods.

Supplementary Table 1: Results for samples obtained (less than or equal to) 12 months prior to therapy (optimal) vs. those obtained >12 months from treatment or shortly after starting therapy (non-optimal). Supplementary Table 2: Cox proportional hazards model of OS adjusting for baseline variables. Supplementary Table 3: Cox proportional hazards model of PFS adjusting for baseline variables. Figure S1: (A) Performance of mutational load across a range of potential thresholds. (B) Receiver Operating Curve (ROC) for mutational loads cutoffs of 3.3 mutations/MB (low mutational load group) and 23.1 mutations/MB (high mutational load group). Figure S2: (A) Mutational load in tumors with cutaneous/unknown primaries in responders vs. non-responders. (B) Mutational load in tumors with non-cutaneous primaries (acral, mucosal, uveal) in responders vs. non-responders. (C) Gene amplifications and deletions in responders vs. non-responders. Figure S3: (A) LRP1B mutations/variants of unknown significance in responders vs. non-responders. (B) Total number of mutations among melanomas with and without LRP1B mutations. (C) Association between number of LRP1B mutations and total mutations in the melanoma TCGA. Figure S4: (A) T cell receptor (TCR) clonality in responders vs. non-responders. (B) T cell fraction in responders vs. non-responders. (C) TCR clonality in responders vs. non-responders in ideal samples, defined as those obtained within 4 months of anti-PD-1/PD-L1 treatment without other prior therapies. (D) T cell fraction in these ideal samples. Figure S5: Mutation load correlated with (A) T cell receptor (TCR) clonality and (B) T cell fraction.

PDF file - 71KB

PDF file - 59KB, Schematic representation of the index patient's treatment history. A timeline detailing the index patient's diagnosis and subsequent systemic therapies is depicted. Time in months is shown below the arrow. The timeline starts at the patient's initial diagnosis.

Supplementary Figures S1 - S10. Supplementary Figure S1. Additional information for Patient 1. Supplementary Figure S2. Additional information for Patient 2. Supplementary Figure S3. Additional information for Patient 3. Supplementary Figure S4. Additional clinical information for Patient 4. Supplementary Figure S5. Characterization of EGFR-RAD51 in NR6 cells. Supplementary Figure S6. Relative stability of EGFR-WT, -L858R, and -RAD51. Supplementary Figure S7. Structural model of EGFR-RAD51 filaments. Supplementary Figure S8. On-target inhibition of EGFR-RAD51 by EGFR TKI. Supplementary Figure S9. Cetuximab inhibits ligand-induced activation of downstream signaling pathways in cells expressing EGFR-RAD51. Supplementary Figure S10. cDNA sequence of EGFR-RAD51.