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The goal of personalized cancer therapy is to treat tumors based on genomic aberrations that drive their survival and progression. Most patients who receive targeted therapies typically develop resistance and disease progression within a year’s time. This review focuses on the heterogeneous mechanisms of therapy resistance to tyrosine kinase inhibitors, endocrine/hormone therapy and checkpoint blockade using non-small cell lung cancer, breast and castration-resistant prostate cancer, and melanoma as classical examples, respectively. In addition, testing for resistance mechanisms and therapeutic approaches to overcoming resistance is addressed.

DNA damage activates cell cycle checkpoint proteins ATR and CHK1 to arrest cell cycle progression, providing time for repair and recovery. Consequently, inhibitors of ATR (ATRi) and CHK1 (CHK1i) enhance damage-induced cell death. Intriguingly, both CHK1i and ATRi alone elicit cytotoxicity in some cell lines. Sensitivity has been attributed to endogenous replications stress, but many more cell lines are sensitive to ATRi than CHK1i. Endogenous activation of the DNA damage response also did not correlate with drug sensitivity. Sensitivity correlated with the appearance of γH2AX, a marker of DNA damage, but without phosphorylation of mitotic markers, contradicting suggestions that the damage is due to premature mitosis. Sensitivity to ATRi has been associated with ATM mutations, but dysfunction in ATM signaling did not correlate with sensitivity. CHK1i and ATRi circumvent replication stress by reactivating stalled replicons, a process requiring a low threshold activity of CDK2. In contrast, γH2AX induced by single agent ATRi and CHK1i requires a high threshold activity CDK2. Hence, phosphorylation of different CDK2 substrates is required for cytotoxicity induced by replication stress plus ATRi/CHK1i as compared to their single agent activity. In summary, sensitivity to ATRi and CHK1i as single agents is elicited by premature hyper-activation of CDK2.

Endometrial cancer is the most common gynecological cancer. To investigate how it suppresses host immune function, we isolated CD8+ T cells from endometrial endometroid carcinomas and adjacent non-cancerous endometrium and determined if the tumor environment regulates cytotoxic capacity. Endometrial carcinomas had increased numbers of CD8+ T cells compared to adjacent non-cancerous endometrium. Tumor CD8+ T cells expressed significantly less granzyme A (GZA), B (GZB), and PD-1 than those in adjacent non-cancerous tissues and also had significantly lower cytotoxic killing of allogeneic target cells. CD103-CD8+ T cells, but not CD103+CD8+ T cells, from both adjacent and tumor tissue were primarily responsible for killing of allogeneic target cells. Secretions recovered from endometrial carcinoma tissues suppressed CD8+ cytotoxic killing and lowered perforin, GZB and PD-1 expression relative to non-tumor CD8+ T cells. Furthermore, tumor secretions contained significantly higher levels of immunosuppressive cytokines including TGFβ than non-tumor tissues. Thus, the tumor microenvironment suppresses cytotoxic killing by CD8+ T cells via the secretion of immunosuppressive cytokines leading to decreased expression of intracellular cytolytic molecules. These studies demonstrate the complexity of CD8+ T cell regulation within the endometrial tumor microenvironment and provide a foundation of information essential for the development of therapeutic strategies for gynecological cancers.

Lynch syndrome (LS) is the most common form of the hereditary colon cancer syndromes. Because of its high prevalence, a nationwide campaign has begun to screen all colorectal cancers for the genetic abnormalities associated with LS. Next to colorectal cancer, endometrial cancer is the most common form of malignancy found in women with LS. Identifying individuals who harbor the well-characterized mismatch-repair gene mutations via immunohistochemistry, microsatellite instability analysis, or direct gene sequencing is critical to managing the LS patient and to surveillance for the development of other associated tumor types. Although many institutions have begun screening all colorectal tumors for LS, the evidence is sufficient to warrant the testing of all endometrial cancers for LS as well. Various testing algorithms, along with genetic-counseling efforts, can lead to a cost-efficient and beneficial screening program.

Pulmonary large-cell carcinoma—a diagnostically and clinically controversial entity—is defined as a non-small-cell carcinoma lacking morphologic differentiation of either adenocarcinoma or squamous cell carcinoma, but suspected to represent an end stage of poor differentiation of these tumor types. Given the recent advances in immunohistochemistry to distinguish adenocarcinoma and squamous cell carcinoma, and the recent insights that several therapeutically relevant genetic alterations are distributed differentially in these tumors, we hypothesized that immunophenotyping may stratify large-cell carcinomas into subsets with distinct profiles of targetable driver mutations. We therefore analyzed 102 large-cell carcinomas by immunohistochemistry for TTF-1 and ΔNp63/p40 as classifiers for adenocarcinoma and squamous cell carcinoma, respectively, and correlated the resulting subtypes with nine therapeutically relevant genetic alterations characteristic of adenocarcinoma (EGFR, KRAS, BRAF, MAP2K1/MEK1, NRAS, ERBB2/HER2 mutations and ALK rearrangements) or more common in squamous cell carcinoma (PIK3CA and AKT1 mutations). The immunomarkers classified large-cell carcinomas as variants of adenocarcinoma (n=62; 60%), squamous cell carcinoma (n=20; 20%) or marker-null (n=20; 20%). Genetic alterations were found in 38 cases (37%), including EGFR (n=1), KRAS (n=30), BRAF (n=2), MAP2K1 (n=1), ALK (n=3) and PIK3CA (n=1). All molecular alterations characteristic of adenocarcinoma occurred in tumors with immunoprofiles of adenocarcinoma or marker-null, but not in tumors with squamous immunoprofiles (combined mutation rate 50% vs 30% vs 0%, respectively; P<0.001), whereas the sole PIK3CA mutation occurred in a tumor with squamous profile (5%). Furthermore, marker-null large-cell carcinomas were associated with significantly inferior disease-free (P<0.001) and overall (P=0.001) survival. In conclusion, the majority (80%) of large-cell carcinomas can be classified by immunomarkers as variants of adenocarcinoma or squamous cell carcinoma, which stratifies these tumors into subsets with a distinct distribution of driver mutations and distinct prognoses. These findings have practical implications for diagnosis, predictive molecular testing and therapy selection.

Background Resistance to immune checkpoint inhibitors (ICIs) has been linked to local immunosuppression independent of major ICI targets (e.g., PD-1). Clinical experience with response prediction based on PD-L1 expression suggests that other factors influence sensitivity to ICIs in non-small cell lung cancer (NSCLC) patients. Methods Tumor specimens from 120 NSCLC patients from 10 institutions were evaluated for PD-L1 expression by immunohistochemistry, and global proliferative profile by targeted RNA-seq. Results Cell proliferation, derived from the mean expression of 10 proliferation-associated genes (namely BUB1, CCNB2, CDK1, CDKN3, FOXM1, KIAA0101, MAD2L1, MELK, MKI67, and TOP2A) , was identified as a marker of response to ICIs in NSCLC. Poorly, moderately, and highly proliferative tumors were somewhat equally represented in NSCLC, with tumors with the highest PD-L1 expression being more frequently moderately proliferative as compared to lesser levels of PD-L1 expression. Proliferation status had an impact on survival in patients with both PD-L1 positive and negative tumors. There was a significant survival advantage for moderately proliferative tumors compared to their combined highly/poorly counterparts ( p  = 0.021). Moderately proliferative PD-L1 positive tumors had a median survival of 14.6 months that was almost twice that of PD-L1 negative highly/poorly proliferative at 7.6 months ( p  = 0.028). Median survival in moderately proliferative PD-L1 negative tumors at 12.6 months was comparable to that of highly/poorly proliferative PD-L1 positive tumors at 11.5 months, but in both instances less than that of moderately proliferative PD-L1 positive tumors. Similar to survival, proliferation status has impact on disease control (DC) in patients with both PD-L1 positive and negative tumors. Patients with moderately versus those with poorly or highly proliferative tumors have a superior DC rate when combined with any classification schema used to score PD-L1 as a positive result (i.e., TPS ≥ 50% or ≥ 1%), and best displayed by a DC rate for moderately proliferative tumors of no less than 40% for any classification of PD-L1 as a negative result. While there is an over representation of moderately proliferative tumors as PD-L1 expression increases this does not account for the improved survival or higher disease control rates seen in PD-L1 negative tumors. Conclusions Cell proliferation is potentially a new biomarker of response to ICIs in NSCLC and is applicable to PD-L1 negative tumors.

Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteria for evaluation. In this study, we propose a deep learning model that automatically classifies the histologic patterns of lung adenocarcinoma on surgical resection slides. Our model uses a convolutional neural network to identify regions of neoplastic cells, then aggregates those classifications to infer predominant and minor histologic patterns for any given whole-slide image. We evaluated our model on an independent set of 143 whole-slide images. It achieved a kappa score of 0.525 and an agreement of 66.6% with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0.485 and agreement of 62.7% on this test set. All evaluation metrics for our model and the three pathologists were within 95% confidence intervals of agreement. If confirmed in clinical practice, our model can assist pathologists in improving classification of lung adenocarcinoma patterns by automatically pre-screening and highlighting cancerous regions prior to review. Our approach can be generalized to any whole-slide image classification task, and code is made publicly available at https://github.com/BMIRDS/deepslide .

Cystic fibrosis is a life-threatening genetic disorder that has been associated with mutations in the CFTR [cystic fibrosis transmembrane conductance regulator (ATP-binding cassette sub-family C, member 7)] gene. Hundreds of CFTR mutations have been detected to date. Current CFTR genotyping assays target a subset of these mutations, particularly a mutation panel recommended by the American College of Medical Genetics for carrier screening of the general population. Fast sequencing of the entire coding sequence in a scalable manner could expand the detection of CFTR mutations and facilitate management of costs and turnaround times in the clinical laboratory. We describe a proof-of-concept CFTR assay that uses PCR target enrichment and next-generation sequencing on the Ion Torrent Personal Genome Machine™ (PGM™) platform. The scalability of the assay was demonstrated, with an average mean depth of coverage ranging from 500× to 3500×, depending on the number of multiplexed patient samples and the Ion Torrent chip used. In a blinded study of 79 previously genotyped patient DNA samples and cell lines, our assay detected most of the mutations, including single-nucleotide variants, small insertions and deletions, and large copy-number variants. The reproducibility was 100% for detecting mutations in independent runs. Our assay demonstrated high specificity, with only 2 false-positive calls (at 2184delA) found in 2 samples caused by a sequencing error in a homopolymer stretch of sequence. The detection rate for variants of unknown significance was very low in the targeted region. With continued optimization and system refinements, PGM sequencing promises to be a powerful, rapid, and scalable means of clinical diagnostic sequencing.

BackgroundImmunotherapy combinations including ipilimumab and nivolumab are now the standard of care for untreated metastatic renal cell carcinoma (mRCC). Biomarkers of response are lacking to predict patients who will have a favorable or unfavorable response to immunotherapy. This study aimed to use the OmniSeq transcriptome-based platform to develop biomarkers of response to immunotherapy.MethodsTwo cohorts of patients were retrospectively collected. These included an investigational cohort of patients with mRCC treated with immune checkpoint inhibitor therapy from five institutions, and a subsequent validation cohort of patients with mRCC treated with combination ipilimumab and nivolumab from two institutions (Duke Cancer Institute and Cleveland Clinic Taussig Cancer Center). Tissue-based RNA sequencing was performed using the OmniSeq Immune Report Card on banked specimens to identify gene signatures and immune checkpoints associated with differential clinical outcomes. A 5-gene expression panel was developed based on the investigational cohort and was subsequently evaluated in the validation cohort. Clinical outcomes including progression-free survival (PFS) and overall survival (OS) were extracted by retrospective chart review. Objective response rate (ORR) was assessed by Response Evaluation Criteria in Solid Tumors (RECIST) V.1.1.ResultsThe initial investigation cohort identified 86 patients with mRCC who received nivolumab (80%, 69/86), ipilimumab/nivolumab (14%, 12/86), or pembrolizumab (6%, 5/86). A gene expression score was created using the top five genes found in responders versus non-responders (FOXP3, CCR4, KLRK1, ITK, TIGIT). The ORR in patients with high gene expression (GEhigh) on the 5-gene panel was 29% (14/48), compared with low gene expression (GElow) 3% (1/38, χ2 p=0.001). The validation cohort was comprised of 62 patients who received ipilimumab/nivolumab. There was no difference between GEhigh and GElow in terms of ORR (44% vs 38.5%), PFS (HR 1.5, 95% CI 0.58 to 3.89), or OS (HR 0.96, 95% CI 0.51 to 1.83). Similarly, no differences in ORR, PFS or OS were observed when patients were stratified by tumor mutational burden (high=top 20%), PD-L1 (programmed death-ligand 1) expression by immunohistochemistry or RNA expression, or CTLA-4 (cytotoxic T-lymphocytes-associated protein 4) RNA expression. The International Metastatic RCC Database Consortium (IMDC) risk score was prognostic for OS but not PFS.ConclusionA 5-gene panel that was associated with improved ORR in a predominantly nivolumab monotherapy population of patients with mRCC was not predictive for radiographic response, PFS, or OS among patients with mRCC treated with ipilimumab and nivolumab.

Purpose: The purpose of this study was to analyze laboratory performance on proficiency testing surveys offered jointly by the College of American Pathologists/American College of Medical Genetics and Genomics biannually for the three common Ashkenazi Jewish founder mutations in the BRCA1 and BRCA2 genes. Methods: Survey responses were analyzed for accuracy of genotype determination and the associated clinical interpretation. Data on an individual laboratory’s participation over time, number of samples tested, turnaround time, and test methodology were also reviewed. Results: Between 2003 and 2012, 23 US laboratories and 39 international laboratories participated. There were six genotyping errors, with a corresponding analytical sensitivity of 99.0% (479/484 challenges; 95% confidence interval: 97.6–99.7%) and an analytic specificity of 99.9% (870/871; 95% confidence interval: 99.4–99.9%). Among the 1,325 clinical interpretations, 92.5% (1,226/1,325; 95% confidence interval: 91.0–93.9%) matched the intended response. Most of the 99 discrepancies—81% (80/99)—incorrectly interpreted the risk for a negative test result as having a lifetime risk of breast cancer “that is the same as that in the general population” instead of “that cannot be determined without BRCA mutation testing of the affected relative.” Conclusion: Clinical laboratories demonstrated excellent analytical sensitivity and specificity. The clinical interpretation requires additional education, focusing on the clinical interpretation of negative test results for these three mutations. Genet Med advance online publication 19 June 2014