Multiscale heterogeneity in gastric adenocarcinoma evolution is an obstacle to precision medicine.

BACKGROUND: Cancer is a somatic evolutionary disease and adenocarcinomas of the stomach and gastroesophageal junction (GC) may serve as a two-dimensional model of cancer expansion, in which tumor subclones are not evenly mixed during tumor progression but rather spatially separated and diversified. We hypothesize that precision medicine efforts are compromised when clinical decisions are based on a single-sample analysis, which ignores the mechanisms of cancer evolution and resulting intratumoral heterogeneity. Using multiregional whole-exome sequencing, we investigated the effect of somatic evolution on intratumoral heterogeneity aiming to shed light on the evolutionary biology of GC. METHODS: The study comprised a prospective discovery cohort of 9 and a validation cohort of 463 GCs. Multiregional whole-exome sequencing was performed using samples form 45 primary tumors and 3 lymph node metastases (range 3-10 tumor samples/patient) of the discovery cohort. RESULTS: In total, the discovery cohort harbored 16,537 non-synonymous mutations. Intratumoral heterogeneity of somatic mutations and copy number variants were present in all tumors of the discovery cohort. Of the non-synonymous mutations, 53-91% were not present in each patient's sample; 399 genes harbored 2-4 different non-synonymous mutations in the same patient; 175 genes showed copy number variations, the majority being heterogeneous, including CD274 (PD-L1). Multi-sample tree-based analyses provided evidence for branched evolution being most complex in a microsatellite instable GC. The analysis of the mode of evolution showed a high degree of heterogeneity in deviation from neutrality within each tumor. We found evidence of parallel evolution and evolutionary trajectories: different mutations of SMAD4 aligned with different subclones and were found only in TP53 mutant GCs. CONCLUSIONS: Neutral and non-neutral somatic evolution shape the mutational landscape in GC along its lateral expansions. It leads to complex spatial intratumoral heterogeneity, where lymph node metastases may stem from different areas of the primary tumor, synchronously. Our findings may have profound effects on future patient management. They illustrate the risk of mis-interpreting tumor genetics based on single-sample analysis and open new avenues for an evolutionary classification of GC, i.e., the discovery of distinct evolutionary trajectories which can be utilized for precision medicine.

Case report: 16-yr life history and genomic evolution of an ER+ HER2- breast cancer.

Metastatic breast cancer is one of the leading causes of cancer-related death in women. Limited studies have been done on the genomic evolution between primary and metastatic breast cancer. We reconstructed the genomic evolution through the 16-yr history of an ER+ HER2- breast cancer patient to investigate molecular mechanisms of disease relapse and treatment resistance after long-term exposure to hormonal therapy. Genomic and transcriptome profiling was performed on primary breast tumor (2002), initial recurrence (2012), and liver metastasis (2015) samples. Cell-free DNA analysis was performed at 11 time points (2015-2017). Mutational analysis revealed a low mutational burden in the primary tumor that doubled at the time of progression, with driver mutations in PI3K-Akt and RAS-RAF signaling pathways. Phylogenetic analysis showed an early branching off between primary tumor and metastasis. Liquid biopsies, although initially negative, started to detect an ESR1 E380Q mutation in 2016 with increasing allele frequency until the end of 2017. Transcriptome analysis revealed 721 (193 up, 528 down) genes to be differentially expressed between primary tumor and first relapse. The most significantly down-regulated genes were TFF1 and PGR, indicating resistance to aromatase inhibitor (AI) therapy. The most up-regulated genes included PTHLH, S100P, and SOX2, promoting tumor growth and metastasis. This phylogenetic reconstruction of the life history of a single patient's cancer as well as monitoring tumor progression through liquid biopsies allowed for uncovering the molecular mechanisms leading to initial relapse, metastatic spread, and treatment resistance.

Stage IV Colorectal Cancer Patients with High Risk Mutation Profiles Survived 16 Months Longer with Individualized Therapies.

Personalized treatment vs. standard of care is much debated, especially in clinical practice. Here we investigated whether overall survival differences in metastatic colorectal cancer patients are explained by tumor mutation profiles or by treatment differences in real clinical practice. Our retrospective study of metastatic colorectal cancer patients of confirmed European ancestry comprised 54 Americans and 54 gender-matched Germans. The Americans received standard of care, and on treatment failure, 35 patients received individualized treatments. The German patients received standard of care only. Tumor mutations, tumor mutation burden and microsatellite status were identified by using the FoundationOne assay or the IDT Pan-Cancer assay. High-risk patients were identified according to the mutational classification by Schell and colleagues. Results: Kaplan-Meier estimates show the high-risk patients to survive 16 months longer under individualized treatments than those under only standard of care, in the median (p < 0.001). Tumor mutation profiles stratify patients by risk groups but not by country. Conclusions: High-risk patients appear to survive significantly longer (p < 0.001) if they receive individualized treatments after the exhaustion of standard of care treatments. Secondly, the tumor mutation landscape in Americans and Germans is congruent and thus warrants the transatlantic exchange of successful treatment protocols and the harmonization of guidelines.

RNA based individualized drug selection in breast cancer patients without patient-matched normal tissue.

BACKGROUND: While standard RNA expression tests stratify patients into risk groups, RNA-Seq can guide personalized drug selection based on expressed mutations, fusion genes, and differential expression (DE) between tumor and normal tissue. However, patient-matched normal tissue may be unavailable. Additionally, biological variability in normal tissue and technological biases may confound results. Therefore, we present normal expression reference data for two sequencing methods that are suitable for breast biopsies. RESULTS: We identified breast cancer related and drug related genes that are expressed uniformly across our normal samples. Large subsets of these genes are identical for formalin fixed paraffin embedded samples and fresh frozen samples. Adipocyte signatures were detected in frozen compared to formalin samples, prepared by surgeons and pathologists, respectively. Gene expression confounded by adipocytes was identified using fat tissue samples. Finally, immune repertoire statistics were obtained for healthy breast, tumor and fat tissues. CONCLUSIONS: Our reference data can be used with patient tumor samples that are asservated and sequenced with a matching aforementioned method. Coefficients of variation are given for normal gene expression. Thus, potential drug selection can be based on confidently overexpressed genes and immune repertoire statistics. MATERIALS AND METHODS: Normal expression from formalin and frozen healthy breast tissue samples using Roche Kapa RiboErase (total RNA) (19 formalin, 9 frozen) and Illumina TruSeq RNA Access (targeted RNA-Seq, aka TruSeq RNA Exome) (11 formalin, 1 frozen), and fat tissue (6 frozen Access). Tumor DE using 10 formalin total RNA tumor samples and 1 frozen targeted RNA tumor sample.

Novel katG mutations causing isoniazid resistance in clinical M. tuberculosis isolates.

We report the discovery and confirmation of 23 novel mutations with previously undocumented role in isoniazid (INH) drug resistance, in catalase-peroxidase (katG) gene of Mycobacterium tuberculosis (Mtb) isolates. With these mutations, a synonymous mutation in fabG1 (g609a), and two canonical mutations, we were able to explain 98% of the phenotypic resistance observed in 366 clinical Mtb isolates collected from four high tuberculosis (TB)-burden countries: India, Moldova, Philippines, and South Africa. We conducted overlapping targeted and whole-genome sequencing for variant discovery in all clinical isolates with a variety of INH-resistant phenotypes. Our analysis showed that just two canonical mutations (katG 315AGC-ACC and inhA promoter-15C-T) identified 89.5% of resistance phenotypes in our collection. Inclusion of the 23 novel mutations reported here, and the previously documented point mutation in fabG1, increased the sensitivity of these mutations as markers of INH resistance to 98%. Only six (2%) of the 332 resistant isolates in our collection did not harbor one or more of these mutations. The third most prevalent substitution, at inhA promoter position -8, present in 39 resistant isolates, was of no diagnostic significance since it always co-occurred with katG 315. 79% of our isolates harboring novel mutations belong to genetic group 1 indicating a higher tendency for this group to go down an uncommon evolutionary path and evade molecular diagnostics. The results of this study contribute to our understanding of the mechanisms of INH resistance in Mtb isolates that lack the canonical mutations and could improve the sensitivity of next generation molecular diagnostics.