Consent document translation expense hinders inclusive clinical trial enrolment.

Patients from historically under-represented racial and ethnic groups are enrolled in cancer clinical trials at disproportionately low rates in the USA1-3. As these patients often have limited English proficiency4-7, we hypothesized that one barrier to their inclusion is the cost to investigators of translating consent documents. To test this hypothesis, we evaluated more than 12,000 consent events at a large cancer centre and assessed whether patients requiring translated consent documents would sign consent documents less frequently in studies lacking industry sponsorship (for which the principal investigator pays the translation costs) than for industry-sponsored studies (for which the translation costs are covered by the sponsor). Here we show that the proportion of consent events for patients with limited English proficiency in studies not sponsored by industry was approximately half of that seen in industry-sponsored studies. We also show that among those signing consent documents, the proportion of consent documents translated into the patient's primary language in studies without industry sponsorship was approximately half of that seen in industry-sponsored studies. The results suggest that the cost of consent document translation in trials not sponsored by industry could be a potentially modifiable barrier to the inclusion of patients with limited English proficiency.

Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring.

Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs. Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases, which has great clinical potential in disease detection and monitoring. Despite the great promise, the sensitive and accurate quantification of tissue-derived cfDNA remains challenging to existing methods due to the limited characterization of tissue methylation and the reliance on unsupervised methods. To fully exploit the clinical potential of tissue-derived cfDNA, here we present one of the largest comprehensive and high-resolution methylation atlas based on 521 noncancer tissue samples spanning 29 major types of human tissues. We systematically identified fragment-level tissue-specific methylation patterns and extensively validated them in orthogonal datasets. Based on the rich tissue methylation atlas, we develop the first supervised tissue deconvolution approach, a deep-learning-powered model, cfSort, for sensitive and accurate tissue deconvolution in cfDNA. On the benchmarking data, cfSort showed superior sensitivity and accuracy compared to the existing methods. We further demonstrated the clinical utilities of cfSort with two potential applications: aiding disease diagnosis and monitoring treatment side effects. The tissue-derived cfDNA fraction estimated from cfSort reflected the clinical outcomes of the patients. In summary, the tissue methylation atlas and cfSort enhanced the performance of tissue deconvolution in cfDNA, thus facilitating cfDNA-based disease detection and longitudinal treatment monitoring.

Targeting more precisely: Improving sensitivity to EGFR inhibitors in NSCLC.

The therapeutic landscape of epidermal growth factor receptor (EGFR)-mutation-positive non-small cell lung cancer (NSCLC) is continually evolving. A recent manuscript in Nature by Robichaux and colleagues1 reports on a structure-based classification of EGFR mutations to help predict sensitivities to EGFR inhibitors in NSCLC that may ultimately improve patient outcomes.

CRKL mediates EML4-ALK signaling and is a potential therapeutic target for ALK-rearranged lung adenocarcinoma.

Anaplastic lymphoma kinase (ALK) gene rearrangements are oncogenic drivers in a small subset of patients with non-small-cell lung cancer (NSCLC). The ALK inhibitors are highly effective in NSCLC patients harboring ALK rearrangements; however, most patients acquire resistance to the therapy following an initial response. Mechanisms of acquired resistance are complex. We used LC-MS/MS-based phosphotyrosine-peptide profiling in the EML4-ALK rearranged H3122 and H2228 cells treated with ALK inhibitors, to identify downstream effectors of ALK. We then used Western blot, siRNA experiments, cell proliferation, viability and migration assays to validate our findings. We identified CRKL as a novel downstream effector of ALK signaling. We demonstrated that CRKL tyrosine phosphorylation was repressed by pharmacological inhibition or small interfering RNA (siRNA) knockdown of ALK in the ALK-rearranged cells. More importantly, CRKL knockdown attenuated their cell proliferation, viability, and migration, but it had no effect on ALK phosphorylation and expression in these cells. Furthermore, CRKL tyrosine phosphorylation was inhibited by dasatinib (an inhibitor of ABL and SRC kinases), which in combination with the ALK inhibitor crizotinib displayed a synergistic inhibitory effect in vitro. In conclusion, our study suggests that CRKL is a key downstream effector of ALK, and combined inhibition of ALK and CRKL may represent an effective strategy for treating ALK-rearranged NSCLC patients.

EMT is associated with, but does not drive resistance to ALK inhibitors among EML4-ALK non-small cell lung cancer.

ALK gene fusion occurs in approximately 3-7% of non-small cell lung cancer (NSCLC). For patients with ALK positive NCSLC, crizotinib and ceritinib are FDA approved ALK inhibitors, however, patients inevitably acquire resistance to such therapies typically within one to two years. Interrogation of in vitro ALK-positive NSCLC cell line models of acquired resistance to first and second-generation ALK inhibitors revealed acquired epithelial-to-mesenchymal transition (EMT) mechanisms. Here we demonstrated that knockdown of upregulated mesenchymal markers in acquired resistant lines decreased the invasive and migratory capabilities of the cells, however, it did not restore sensitivity to ALK inhibitors. Removing drug for 5 weeks from H3122 cell line that acquired resistance to ceritinib restored its sensitivity to ceritinib. In addition, HSP90 inhibitors ganetespib and 17-AAG were potent in inducing cell death in cell lines resistant to crizotinib and ceritinib. Taken together, EMT does not drive resistance to ALK inhibitors and HSP90 inhibition demonstrates more efficacy when further ALK inhibition may not. This study warrants more exploration of HSP90 inhibitors for ALK-positive patients who progress on 1st and 2nd line ALK inhibitor therapy.

Development of a robust RNA-based classifier to accurately determine ER, PR, and HER2 status in breast cancer clinical samples.

Breast cancers are categorized into three subtypes based on protein expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2/ERBB2). Patients enroll onto experimental clinical trials based on ER, PR, and HER2 status and, as receptor status is prognostic and defines treatment regimens, central receptor confirmation is critical for interpreting results from these trials. Patients enrolling onto experimental clinical trials in the metastatic setting often have limited available archival tissue that might better be used for comprehensive molecular profiling rather than slide-intensive reconfirmation of receptor status. We developed a Random Forests-based algorithm using a training set of 158 samples with centrally confirmed IHC status, and subsequently validated this algorithm on multiple test sets with known, locally determined IHC status. We observed a strong correlation between target mRNA expression and IHC assays for HER2 and ER, achieving an overall accuracy of 97 and 96%, respectively. For determining PR status, which had the highest discordance between central and local IHC, incorporation of expression of co-regulated genes in a multivariate approach added predictive value, outperforming the single, target gene approach by a 10% margin in overall accuracy. Our results suggest that multiplexed qRT-PCR profiling of ESR1, PGR, and ERBB2 mRNA, along with several other subtype associated genes, can effectively confirm breast cancer subtype, thereby conserving tumor sections and enabling additional biomarker data to be obtained from patients enrolled onto experimental clinical trials.

Oncogenic drivers, targeted therapies, and acquired resistance in non-small-cell lung cancer.

In the past decade, a shift toward targeted therapies in non-small-cell lung cancer following molecular profiling has dramatically changed the way advanced adenocarcinoma is treated. However, tumor cells inevitably acquire resistance to such therapies, circumventing any sustained clinical benefit. As the genomic classification of lung cancer continues to evolve and as the mechanisms of acquired resistance to targeted therapies become elucidated and more improved target-specific drugs come into sight, the future will see more promising results from the clinic through the development of new therapeutic strategies to overcome, or prevent the development of, resistance for lung cancer patients.