The present invention relates to methods and systems for the analysis of nucleic acids present in biological samples, and more specifically, relates to clustering melt curves derived from high resolution thermal melt analysis performed on a sample of nucleic acids, the resulting clusters being usable, in one embodiment, for analyzing the sequences of nucleic acids and to classify their genotypes that are useful for determining the identity of the genotype of a nucleic acid that is present in a biological sample.

A method is disclosed for predicting in advance whether a melanoma patient is likely to benefit from high dose IL2 therapy in treatment of the cancer. The method makes use of mass spectrometry data obtained from a blood-based sample of the patient and a computer configured as a classifier and making use of a reference set of mass spectral data obtained from a development set of blood-based samples from other melanoma patients. A variety of classifiers for making this prediction are disclosed, including a classifier developed from a set of blood-based samples obtained from melanoma patients treated with high dose IL2 as well as melanoma patients treated with an anti-PD-1 immunotherapy drug. The classifiers developed from anti-PD-1 and IL2 patient sample cohorts can also be used in combination to guide treatment of a melanoma patient.

A method is disclosed for predicting in advance whether a melanoma patient is likely to benefit from high dose IL2 therapy in treatment of the cancer. The method makes use of mass spectrometry data obtained from a blood-based sample of the patient and a computer configured as a classifier and making use of a reference set of mass spectral data obtained from a development set of blood-based samples from other melanoma patients. A variety of classifiers for making this prediction are disclosed, including a classifier developed from a set of blood-based samples obtained from melanoma patients treated with high dose IL2 as well as melanoma patients treated with an anti-PD-1 immunotherapy drug. The classifiers developed from anti-PD-1 and IL2 patient sample cohorts can also be used in combination to guide treatment of a melanoma patient.

Polynucleotide sequencing generates multiple reads of a polynucleotide molecule. Many or all of the reads contain errors. Trace reconstruction takes multiple reads generated by a polynucleotide sequencer and uses those multiple reads to reconstruct accurately the nucleotide sequence of the polynucleotide molecule. Some reads may contain errors that cannot be corrected. Thus, there may be reads that can be used throughout their entire length and other reads that have indeterminant errors which cannot be corrected. Rather than discarding the entire read when an indeterminant error is found, the portion of the read with the error is skipped and the sequence of the read following the error is used to reconstruct the trace. The amount of the read skipped is determined by the location of subsequence after the error that matches a consensus sequence of the other reads. Analysis resumes at a location determined by the location of the match.

Polynucleotide sequencing generates multiple reads of a polynucleotide molecule. Many or all of the reads contain errors. Trace reconstruction takes multiple reads generated by a polynucleotide sequencer and uses those multiple reads to reconstruct accurately the nucleotide sequence of the polynucleotide molecule. Some reads may contain errors that cannot be corrected. Thus, there may be reads that can be used throughout their entire length and other reads that have indeterminant errors which cannot be corrected. Rather than discarding the entire read when an indeterminant error is found, the portion of the read with the error is skipped and the sequence of the read following the error is used to reconstruct the trace. The amount of the read skipped is determined by the location of subsequence after the error that matches a consensus sequence of the other reads. Analysis resumes at a location determined by the location of the match.

Cell free nucleic acids from a test sample obtained from an individual are analyzed to identify possible fusion events. Cell free nucleic acids are sequenced and processed to generate fragments. Fragments are decomposed into kmers and the kmers are either analyzed de novo or compared to targeted nucleic acid sequences that are known to be associated with fusion gene pairs of interest. Thus, kmers that may have originated from a fusion event can be identified. These kmers are consolidated to generate gene ranges from various genes that match sequences in the fragment. A candidate fusion event can be called given the spanning of one or more gene ranges across the fragment.

Cell free nucleic acids from a test sample obtained from an individual are analyzed to identify possible fusion events. Cell free nucleic acids are sequenced and processed to generate fragments. Fragments are decomposed into kmers and the kmers are either analyzed de novo or compared to targeted nucleic acid sequences that are known to be associated with fusion gene pairs of interest. Thus, kmers that may have originated from a fusion event can be identified. These kmers are consolidated to generate gene ranges from various genes that match sequences in the fragment. A candidate fusion event can be called given the spanning of one or more gene ranges across the fragment.

The present disclosure relates to an apparatus including one or more processors configured to receive a digitized image of a region of tissue demonstrating a disease, and containing cellular structures represented in the digitized image, each of the cellular structures being associated with a cell category of a plurality of cell categories; select a cellular structure of the cellular structures based on the cell category for the cellular structure; for the cellular structure selected, compute a set of contextual features; assign, based on the set of contextual features, the cellular structure to at least one cluster of a plurality of clusters; compute cluster features, the cluster features describing characteristics of the at least one cluster of the plurality of clusters; and generate a prediction that describes a pathologic or phenotypic state of the disease based, at least in part, on the cluster features and/or the set of contextual features.

The present disclosure relates to an apparatus including one or more processors configured to receive a digitized image of a region of tissue demonstrating a disease, and containing cellular structures represented in the digitized image, each of the cellular structures being associated with a cell category of a plurality of cell categories; select a cellular structure of the cellular structures based on the cell category for the cellular structure; for the cellular structure selected, compute a set of contextual features; assign, based on the set of contextual features, the cellular structure to at least one cluster of a plurality of clusters; compute cluster features, the cluster features describing characteristics of the at least one cluster of the plurality of clusters; and generate a prediction that describes a pathologic or phenotypic state of the disease based, at least in part, on the cluster features and/or the set of contextual features.

A system, a method and a non-transitory computer readable storage medium for base calling are described. The base calling method includes processing through a neural network first image data comprising images of clusters and their surrounding background captured by a sequencing system for one or more sequencing cycles of a sequencing run. The base calling method further includes producing a base call for one or more of the clusters of the one or more sequencing cycles of the sequencing run.