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PURPOSE Aumolertinib (formerly almonertinib; HS-10296) is a novel third-generation epidermal growth factor receptor tyrosine kinase inhibitor approved in China. This double-blind phase III trial evaluated the efficacy and safety of aumolertinib compared with gefitinib as a first-line treatment for locally advanced or metastatic EGFR-mutated non–small-cell lung cancer (NSCLC; ClinicalTrials.gov identifier: NCT03849768 ). METHODS Patients at 53 sites in China were randomly assigned 1:1 to receive either aumolertinib (110 mg) or gefitinib (250 mg) once daily. The primary end point was progression-free survival (PFS) per investigator assessment. RESULTS A total of 429 patients who were naïve to treatment for locally advanced or metastatic NSCLC were enrolled. PFS was significantly longer with aumolertinib compared with gefitinib (hazard ratio, 0.46; 95% CI, 0.36 to 0.60; P < .0001). The median PFS with aumolertinib was 19.3 months (95% CI, 17.8 to 20.8) versus 9.9 months with gefitinib (95% CI, 8.3 to 12.6). Objective response rate and disease control rate were similar in the aumolertinib and gefitinib groups (objective response rate, 73.8% and 72.1%, respectively; disease control rate, 93.0% and 96.7%, respectively). The median duration of response was 18.1 months (95% CI, 15.2 to not applicable) with aumolertinib versus 8.3 months (95% CI, 6.9 to 11.1) with gefitinib. Adverse events of grade ≥ 3 severity (any cause) were observed in 36.4% and 35.8% of patients in the aumolertinib and gefitinib groups, respectively. Rash and diarrhea (any grade) were observed in 23.4% and 16.4% of patients who received aumolertinib compared with 41.4% and 35.8% of those who received gefitinib, respectively. CONCLUSION Aumolertinib is a well-tolerated third-generation epidermal growth factor receptor tyrosine kinase inhibitor that could serve as a treatment option for EGFR-mutant NSCLC in the first-line setting.

Advanced and metastatic squamous cell carcinomas (SCC) are common and difficult-to-treat malignancies. We assessed 75 immunotherapy-treated patients with SCC from a clinically annotated database of 2,651 patients, as well as 9,407 patients from a deidentified database for molecular features that might influence checkpoint blockade response. SCCs had higher tumor mutational burdens (TMB) than non-SCCs (P < 0.0001). Cutaneous SCCs had the highest TMB (P < 0.0001), with 41.3% demonstrating a very high TMB (≥50 mutations/Mb). In immunotherapy-treated patients with SCC, higher TMB (≥12 mutations/Mb) correlated with a trend to higher clinical benefit rate [stable disease ≥ 6 months or partial/complete remission; 60% vs. 29%; (high vs. low TMB); P = 0.06] and significantly longer median time-to-treatment failure (TTF; 9.9 vs. 4.4 months; P = 0.0058). Cutaneous SCCs had the highest clinical benefit [11/15 patients (73%) vs. 20/60 (33%) non-cutaneous (P = 0.008)], TTF (P = 0.0015), and overall survival (P = 0.06) with immunotherapy treatment. In conclusion, among a diverse set of SCCs, higher TMB and cutaneous disease associated with better immunotherapy outcome.

Supplemental Tables 1-3 and Supplemental Figures 1-5

PDF file 62K, MCL1 overexpression enhances docetaxel resistance in non-amplified TNBC cell lines

PDF file 77K, Association of gene alterations with gene expression

XLSX file 706K, This file contains 8 supplementary data tables which include patient demographics, genes and alterations assessed by NGS, Nanostring gene expression data, and bioinformatics analyses

There is growing interest in delivering genomically informed cancer therapy. Our aim was to determine the concordance of genomic alterations between primary and recurrent breast cancer. Targeted next-generation sequencing was performed on formalin-fixed paraffin-embedded (FFPE) samples, profiling 3,320 exons of 182 cancer-related genes plus 37 introns from 14 genes often rearranged in cancer. Point mutations, indels, copy-number alterations (CNA), and select rearrangements were assessed in 74 tumors from 43 patients (36 primary and 38 recurrence/metastases). Alterations potentially targetable with established or investigational therapeutics were considered “actionable.” Alterations were detected in 55 genes (mean 3.95 alterations/sample, range 1–12), including mutations in PIK3CA, TP53, ARID1A, PTEN, AKT1, NF1, FBXW7, and FGFR3 and amplifications in MCL1, CCND1, FGFR1, MYC, IGF1R, MDM2, MDM4, AKT3, CDK4, and AKT2. In 33 matched primary and recurrent tumors, 97 of 112 (86.6%) somatic mutations were concordant. Of identified CNAs, 136 of 159 (85.5%) were concordant: 37 (23.3%) were concordant, but below the reporting threshold in one of the matched samples, and 23 (14.5%) discordant. There was an increased frequency of CDK4/MDM2 amplifications in recurrences, as well as gains and losses of other actionable alterations. Forty of 43 (93%) patients had actionable alterations that could inform targeted treatment options. In conclusion, deep genomic profiling of cancer-related genes reveals potentially actionable alterations in most patients with breast cancer. Overall there was high concordance between primary and recurrent tumors. Analysis of recurrent tumors before treatment may provide additional insights, as both gains and losses of targets are observed. Mol Cancer Ther; 13(5); 1382–9. ©2014 AACR.

Inflammatory myofibroblastic tumor (IMT) is a neoplasm that typically occurs in children. The genetic landscape of this tumor is incompletely understood and therapeutic options are limited. Although 50% of IMTs harbor anaplastic lymphoma kinase (ALK) rearrangements, no therapeutic targets have been identified in ALK-negative tumors. We report for the first time that IMTs harbor other actionable targets, including ROS1 and PDGFRβ fusions. We detail the case of an 8-year-old boy with treatment-refractory ALK-negative IMT. Molecular tumor profiling revealed a ROS1 fusion, and he had a dramatic response to the ROS1 inhibitor crizotinib. This case prompted assessment of a larger series of IMTs. Next-generation sequencing revealed that 85% of cases evaluated harbored kinase fusions involving ALK, ROS1, or PDGFRβ. Our study represents the most comprehensive genetic analysis of IMTs to date and also provides a rationale for routine molecular profiling of these tumors to detect therapeutically actionable kinase fusions.Significance: Our study describes the most comprehensive genomics-based evaluation of IMT to date. Because there is no “standard-of-care” therapy for IMT, the identification of actionable genomic alterations, in addition to ALK, is expected to redefine management strategies for patients with this disease. Cancer Discov; 4(8); 889–95. ©2014 AACR.See related commentary by Le and Doebele, p. 870This article is highlighted in the In This Issue feature, p. 855

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

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.

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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.

Adverse events of any grade.

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.

PDF - 138K, Adjuvant systemic treatments.

PDF file - 77KB

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.

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.

PDF file - 92K, Effects of HER2 overexpression on the sensitivity of HCC827 cells to EGFR TKIs

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 - 91K, Levels of HER2 affect the sensitivity of EGFR-mutant NSCLC cells to EGFR TKIs

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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.

PDF file - 137KB, Summary of genomic coordinates for the kinase fusions identified in this study. The sample number is depicted in the far left column. For tumor samples in which a kinase fusion was identified, the genomic coordinates for of each of the fusion genes is indicated. There were no other recurrent alterations. *The FN1-ALK fusion was detected with RNA sequencing. #Sufficient material was available to verify these kinase fusions with RNA sequencing.

PDF file - 53KB, FoundationOne panel. This targeted next generation sequencing platform evaluates for 3769 exons of 236 cancer genes and 47 introns of 19 commonly rearranged genes, including 8 tyrosine kinases.

PDF file 45K, Association of high Ki67 with basal-like status

PDF - 36K, Samples tested.

This document contains Supplementary Figures 1-6 and Supplementary Table 1. Supplementary Figure 1. Sequencing reads of EGFR-KDD in index patient with lung adenocarcinoma. Supplementary Figure 2. cDNA sequence of EGFR-KDD. Supplementary Figure 3. Sequencing reads identifying EGFR-KDD in a lung adenocarcinoma tumor from The Cancer Genome Atlas. Supplementary Figure 4. Autophosphorylation of endogenous EGFR-KDD in A1235 cells. Supplementary Figure 5. Colony formation of NR6 cells expressing EGFR variants. Supplementary Figure 6. Efficacy of EGFR TKIs in endogenous and ectopic models of the EGFR-KDD. Supplementary Table 1. Results of MTT curve fitting from Prism.

Genomic alterations in lung adenocarcinoma displayed in Figure 2A.

PDF file 26K, Association of node status with RFS and OS, stratified by menopausal status

This document contains Supplementary Methods, Supplementary Figure Legends, and Supplementary Table Legend.

Focal amplification and activating point mutation of the MET gene are well-characterized oncogenic drivers that confer susceptibility to targeted MET inhibitors. Recurrent somatic splice site alterations at MET exon 14 (METex14) that result in exon skipping and MET activation have been characterized, but their full diversity and prevalence across tumor types are unknown. Here, we report analysis of tumor genomic profiles from 38,028 patients to identify 221 cases with METex14 mutations (0.6%), including 126 distinct sequence variants. METex14 mutations are detected most frequently in lung adenocarcinoma (3%), but also frequently in other lung neoplasms (2.3%), brain glioma (0.4%), and tumors of unknown primary origin (0.4%). Further in vitro studies demonstrate sensitivity to MET inhibitors in cells harboring METex14 alterations. We also report three new patient cases with METex14 alterations in lung or histiocytic sarcoma tumors that showed durable response to two different MET-targeted therapies. The diversity of METex14 mutations indicates that diagnostic testing via comprehensive genomic profiling is necessary for detection in a clinical setting.Significance: Here we report the identification of diverse exon 14 splice site alterations in MET that result in constitutive activity of this receptor and oncogenic transformation in vitro. Patients whose tumors harbored these alterations derived meaningful clinical benefit from MET inhibitors. Collectively, these data support the role of METex14 alterations as drivers of tumorigenesis, and identify a unique subset of patients likely to derive benefit from MET inhibitors. Cancer Discov; 5(8); 850–9. ©2015 AACR.See related commentary by Ma, p. 802.See related article by Paik et al., p. 842.This article is highlighted in the In This Issue feature, p. 783

PDF file - 87KB, Changes in the indicated laboratory parameters after 3 cycles of crizotinib. HgB: hemoglobin, Hct: hematocrit, MCV: mean corpuscular volume, ESR: erythrocyte sedimentation rate, LDH: lactate dehydrogenase.

PDF 118K, TSC1 truncation in the RD of a TNBC after NAC

Supplementary Table S1A: Genes analyzed for base substitutions, short insertions/deletions, and copy number alterations (DNA). Supplementary Table S1B: Genes analyzed for rearrangements (DNA).

PDF file 1474K, MEK activity is required for MYC-induced soft agar colony formation

PDF - 168K, Detailed analysis of matched primary and recurrent/metastatic breast tumors.